import json
from os import path
from collections import defaultdict
from libc.string cimport memset
from cymem.cymem cimport Pool
from thinc.typedefs cimport atom_t, weight_t
from thinc.extra.eg cimport Example
from thinc.structs cimport ExampleC
from thinc.linear.avgtron cimport AveragedPerceptron
from thinc.linalg cimport VecVec
from .typedefs cimport attr_t
from .tokens.doc cimport Doc
from .attrs cimport TAG
from .parts_of_speech cimport NO_TAG, ADJ, ADV, ADP, CONJ, DET, NOUN, NUM, PRON
from .parts_of_speech cimport VERB, X, PUNCT, EOL, SPACE
from .attrs cimport *
from .util import get_package
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):
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:
"""A part-of-speech tagger for English"""
@classmethod
def read_config(cls, data_dir):
return json.load(open(path.join(data_dir, 'pos', 'config.json')))
@classmethod
def default_templates(cls):
return (
(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,),
)
@classmethod
def blank(cls, vocab, templates):
model = TaggerModel(templates)
return cls(vocab, model)
@classmethod
def load(cls, data_dir, vocab):
return cls.from_package(get_package(data_dir), vocab=vocab)
@classmethod
def from_package(cls, pkg, vocab):
# TODO: templates.json deprecated? not present in latest package
# templates = cls.default_templates()
templates = pkg.load_json(('pos', 'templates.json'), default=cls.default_templates())
model = TaggerModel(templates)
if pkg.has_file('pos', 'model'):
model.load(pkg.file_path('pos', 'model'))
return cls(vocab, model)
def __init__(self, Vocab vocab, TaggerModel model):
self.vocab = vocab
self.model = model
# 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
@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.
Args:
tokens (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(&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):
for doc in stream:
self(doc)
yield doc
def train(self, Doc tokens, object gold_tag_strs):
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.updateC(&eg.c)
self.vocab.morphology.assign_tag(&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