spaCy/spacy/tagger.pyx

254 lines
7.4 KiB
Cython

# coding: utf8
from __future__ import unicode_literals
from collections import defaultdict
from cymem.cymem cimport Pool
from thinc.typedefs cimport atom_t
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
from .gold cimport GoldParse
from .attrs cimport *
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):
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]:
self.update_weight(feat.key, best, -feat.value)
self.update_weight(feat.key, guess, feat.value)
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."""
def __init__(self, Vocab vocab, TaggerModel model=None, **cfg):
"""Create a Tagger.
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:
model = TaggerModel(cfg.get('features', self.feature_templates),
L1=0.0)
self.vocab = vocab
self.model = model
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
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.
doc (Doc): The tokens to be tagged.
"""
if tokens.length == 0:
return 0
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)
self.model.set_scoresC(eg.c.scores,
eg.c.features, eg.c.nr_feat)
guess = VecVec.arg_max_if_true(eg.c.scores, eg.c.is_valid, eg.c.nr_class)
self.vocab.morphology.assign_tag_id(&tokens.c[i], guess)
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.
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
def update(self, Doc tokens, GoldParse gold, itn=0):
"""Update the statistical model, with tags supplied for the given document.
doc (Doc): The document to update on.
gold (GoldParse): Manager for the gold-standard tags.
RETURNS (int): Number of tags predicted correctly.
"""
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) ]
self.model.set_scoresC(eg.c.scores,
eg.c.features, eg.c.nr_feat)
self.model.update(eg)
self.vocab.morphology.assign_tag_id(&tokens.c[i], eg.guess)
correct += eg.cost == 0
self.freqs[TAG][tokens.c[i].tag] += 1
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,),
)