spaCy/spacy/tagger.pyx

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