mirror of
https://github.com/explosion/spaCy.git
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308 lines
9.0 KiB
Cython
308 lines
9.0 KiB
Cython
import json
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# coding: utf8
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from __future__ import unicode_literals
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from collections import defaultdict
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from cymem.cymem cimport Pool
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from thinc.typedefs cimport atom_t
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from thinc.extra.eg cimport Example
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from thinc.structs cimport ExampleC
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from thinc.linear.avgtron cimport AveragedPerceptron
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from thinc.linalg cimport VecVec
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from .tokens.doc cimport Doc
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from .attrs cimport TAG
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from .gold cimport GoldParse
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from .attrs cimport *
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cpdef enum:
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P2_orth
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P2_cluster
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P2_shape
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P2_prefix
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P2_suffix
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P2_pos
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P2_lemma
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P2_flags
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P1_orth
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P1_cluster
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P1_shape
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P1_prefix
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P1_suffix
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P1_pos
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P1_lemma
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P1_flags
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W_orth
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W_cluster
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W_shape
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W_prefix
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W_suffix
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W_pos
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W_lemma
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W_flags
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N1_orth
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N1_cluster
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N1_shape
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N1_prefix
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N1_suffix
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N1_pos
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N1_lemma
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N1_flags
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N2_orth
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N2_cluster
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N2_shape
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N2_prefix
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N2_suffix
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N2_pos
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N2_lemma
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N2_flags
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N_CONTEXT_FIELDS
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cdef class TaggerModel(AveragedPerceptron):
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def update(self, Example eg):
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self.time += 1
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guess = eg.guess
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best = VecVec.arg_max_if_zero(eg.c.scores, eg.c.costs, eg.c.nr_class)
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if guess != best:
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for feat in eg.c.features[:eg.c.nr_feat]:
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self.update_weight(feat.key, best, -feat.value)
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self.update_weight(feat.key, guess, feat.value)
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cdef void set_featuresC(self, ExampleC* eg, const TokenC* tokens, int i) except *:
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_fill_from_token(&eg.atoms[P2_orth], &tokens[i-2])
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_fill_from_token(&eg.atoms[P1_orth], &tokens[i-1])
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_fill_from_token(&eg.atoms[W_orth], &tokens[i])
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_fill_from_token(&eg.atoms[N1_orth], &tokens[i+1])
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_fill_from_token(&eg.atoms[N2_orth], &tokens[i+2])
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eg.nr_feat = self.extracter.set_features(eg.features, eg.atoms)
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cdef inline void _fill_from_token(atom_t* context, const TokenC* t) nogil:
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context[0] = t.lex.lower
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context[1] = t.lex.cluster
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context[2] = t.lex.shape
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context[3] = t.lex.prefix
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context[4] = t.lex.suffix
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context[5] = t.tag
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context[6] = t.lemma
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if t.lex.flags & (1 << IS_ALPHA):
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context[7] = 1
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elif t.lex.flags & (1 << IS_PUNCT):
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context[7] = 2
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elif t.lex.flags & (1 << LIKE_URL):
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context[7] = 3
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elif t.lex.flags & (1 << LIKE_NUM):
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context[7] = 4
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else:
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context[7] = 0
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cdef class Tagger:
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"""
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Annotate part-of-speech tags on Doc objects.
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"""
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@classmethod
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def load(cls, path, vocab, require=False):
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"""
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Load the statistical model from the supplied path.
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Arguments:
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path (Path):
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The path to load from.
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vocab (Vocab):
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The vocabulary. Must be shared by the documents to be processed.
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require (bool):
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Whether to raise an error if the files are not found.
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Returns (Tagger):
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The newly created object.
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"""
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# TODO: Change this to expect config.json when we don't have to
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# support old data.
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path = path if not isinstance(path, basestring) else pathlib.Path(path)
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if (path / 'templates.json').exists():
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with (path / 'templates.json').open('r', encoding='utf8') as file_:
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templates = json.load(file_)
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elif require:
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raise IOError(
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"Required file %s/templates.json not found when loading Tagger" % str(path))
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else:
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templates = cls.feature_templates
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self = cls(vocab, model=None, feature_templates=templates)
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if (path / 'model').exists():
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self.model.load(str(path / 'model'))
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elif require:
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raise IOError(
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"Required file %s/model not found when loading Tagger" % str(path))
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return self
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def __init__(self, Vocab vocab, TaggerModel model=None, **cfg):
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"""
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Create a Tagger.
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Arguments:
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vocab (Vocab):
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The vocabulary object. Must be shared with documents to be processed.
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model (thinc.linear.AveragedPerceptron):
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The statistical model.
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Returns (Tagger):
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The newly constructed object.
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"""
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if model is None:
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model = TaggerModel(cfg.get('features', self.feature_templates),
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L1=0.0)
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self.vocab = vocab
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self.model = model
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self.model.l1_penalty = 0.0
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# TODO: Move this to tag map
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self.freqs = {TAG: defaultdict(int)}
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for tag in self.tag_names:
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self.freqs[TAG][self.vocab.strings[tag]] = 1
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self.freqs[TAG][0] = 1
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self.cfg = cfg
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@property
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def tag_names(self):
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return self.vocab.morphology.tag_names
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def __reduce__(self):
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return (self.__class__, (self.vocab, self.model), None, None)
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def tag_from_strings(self, Doc tokens, object tag_strs):
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cdef int i
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for i in range(tokens.length):
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self.vocab.morphology.assign_tag(&tokens.c[i], tag_strs[i])
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tokens.is_tagged = True
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tokens._py_tokens = [None] * tokens.length
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def __call__(self, Doc tokens):
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"""
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Apply the tagger, setting the POS tags onto the Doc object.
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Arguments:
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doc (Doc): The tokens to be tagged.
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Returns:
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None
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"""
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if tokens.length == 0:
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return 0
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cdef Pool mem = Pool()
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cdef int i, tag
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cdef Example eg = Example(nr_atom=N_CONTEXT_FIELDS,
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nr_class=self.vocab.morphology.n_tags,
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nr_feat=self.model.nr_feat)
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for i in range(tokens.length):
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if tokens.c[i].pos == 0:
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self.model.set_featuresC(&eg.c, tokens.c, i)
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self.model.set_scoresC(eg.c.scores,
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eg.c.features, eg.c.nr_feat)
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guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
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self.vocab.morphology.assign_tag_id(&tokens.c[i], guess)
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eg.fill_scores(0, eg.c.nr_class)
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tokens.is_tagged = True
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tokens._py_tokens = [None] * tokens.length
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def pipe(self, stream, batch_size=1000, n_threads=2):
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"""
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Tag a stream of documents.
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Arguments:
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stream: The sequence of documents to tag.
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batch_size (int):
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The number of documents to accumulate into a working set.
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n_threads (int):
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The number of threads with which to work on the buffer in parallel,
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if the Matcher implementation supports multi-threading.
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Yields:
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Doc Documents, in order.
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"""
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for doc in stream:
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self(doc)
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yield doc
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def update(self, Doc tokens, GoldParse gold, itn=0):
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"""
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Update the statistical model, with tags supplied for the given document.
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Arguments:
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doc (Doc):
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The document to update on.
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gold (GoldParse):
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Manager for the gold-standard tags.
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Returns (int):
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Number of tags correct.
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"""
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gold_tag_strs = gold.tags
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assert len(tokens) == len(gold_tag_strs)
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for tag in gold_tag_strs:
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if tag != None and tag not in self.tag_names:
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msg = ("Unrecognized gold tag: %s. tag_map.json must contain all "
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"gold tags, to maintain coarse-grained mapping.")
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raise ValueError(msg % tag)
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golds = [self.tag_names.index(g) if g is not None else -1 for g in gold_tag_strs]
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cdef int correct = 0
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cdef Pool mem = Pool()
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cdef Example eg = Example(
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nr_atom=N_CONTEXT_FIELDS,
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nr_class=self.vocab.morphology.n_tags,
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nr_feat=self.model.nr_feat)
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for i in range(tokens.length):
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self.model.set_featuresC(&eg.c, tokens.c, i)
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eg.costs = [ 1 if golds[i] not in (c, -1) else 0 for c in xrange(eg.nr_class) ]
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self.model.set_scoresC(eg.c.scores,
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eg.c.features, eg.c.nr_feat)
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self.model.update(eg)
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self.vocab.morphology.assign_tag_id(&tokens.c[i], eg.guess)
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correct += eg.cost == 0
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self.freqs[TAG][tokens.c[i].tag] += 1
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eg.fill_scores(0, eg.c.nr_class)
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eg.fill_costs(0, eg.c.nr_class)
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tokens.is_tagged = True
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tokens._py_tokens = [None] * tokens.length
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return correct
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feature_templates = (
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(W_orth,),
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(P1_lemma, P1_pos),
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(P2_lemma, P2_pos),
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(N1_orth,),
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(N2_orth,),
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(W_suffix,),
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(W_prefix,),
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(P1_pos,),
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(P2_pos,),
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(P1_pos, P2_pos),
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(P1_pos, W_orth),
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(P1_suffix,),
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(N1_suffix,),
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(W_shape,),
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(W_cluster,),
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(N1_cluster,),
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(N2_cluster,),
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(P1_cluster,),
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(P2_cluster,),
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(W_flags,),
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(N1_flags,),
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(N2_flags,),
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(P1_flags,),
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(P2_flags,),
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)
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