mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-12 04:38:28 +03:00
* Refactor CharacterTagger
This commit is contained in:
Matthew Honnibal
parent
92e9134603
commit
fab538672e
127
spacy/tagger.pyx
127
spacy/tagger.pyx
|
|
@ -1,4 +1,5 @@
|
|||
from __future__ import unicode_literals
|
||||
cimport cython
|
||||
import json
|
||||
from os import path
|
||||
from collections import defaultdict
|
||||
|
|
@ -10,6 +11,7 @@ from thinc.extra.eg cimport Example
|
|||
from thinc.structs cimport ExampleC
|
||||
from thinc.linear.avgtron cimport AveragedPerceptron
|
||||
from thinc.linalg cimport VecVec
|
||||
from thinc.structs cimport FeatureC
|
||||
|
||||
from .typedefs cimport attr_t
|
||||
from .tokens.doc cimport Doc
|
||||
|
|
@ -125,14 +127,29 @@ cdef class TaggerNeuralNet(NeuralNet):
|
|||
|
||||
cdef class CharacterTagger(NeuralNet):
|
||||
def __init__(self, n_classes,
|
||||
depth=4, hidden_width=50,
|
||||
chars_width=10,
|
||||
words_width=20, shape_width=5, suffix_width=5, tags_width=20,
|
||||
learn_rate=0.1):
|
||||
input_length = 5 * chars_width * self.chars_per_word + 2 * tags_width
|
||||
widths = [input_length] + [hidden_width] * depth + [n_classes]
|
||||
depth=3, hidden_width=100, chars_width=5, tags_width=10, learn_rate=0.1,
|
||||
left_window=2, right_window=2, tags_window=10, chars_per_word=8):
|
||||
self.chars_per_word = chars_per_word
|
||||
self.chars_width = chars_width
|
||||
self.tags_width = tags_width
|
||||
self.left_window = left_window
|
||||
self.right_window = right_window
|
||||
self.tags_window = tags_window
|
||||
self.depth = depth
|
||||
self.hidden_width = hidden_width
|
||||
|
||||
input_length = self.left_window * self.chars_width * self.chars_per_word \
|
||||
+ self.right_window * self.chars_width * self.chars_per_word \
|
||||
+ 1 * self.chars_width * self.chars_per_word \
|
||||
+ self.tags_window * self.tags_width
|
||||
|
||||
widths = [input_length] + [self.hidden_width] * self.depth + [n_classes]
|
||||
|
||||
vector_widths = [chars_width, tags_width]
|
||||
slots = [0] * 5 * self.chars_per_word + [1] * 2
|
||||
slots = [0] * self.left_window * self.chars_per_word \
|
||||
+ [0] * self.right_window * self.chars_per_word \
|
||||
+ [0] * 1 * self.chars_per_word \
|
||||
+ [1] * self.tags_window
|
||||
NeuralNet.__init__(
|
||||
self,
|
||||
widths,
|
||||
|
|
@ -141,35 +158,29 @@ cdef class CharacterTagger(NeuralNet):
|
|||
rho=1e-6,
|
||||
update_step='sgd')
|
||||
|
||||
cdef void set_featuresC(self, ExampleC* eg, const TokenC* tokens, object strings, int i) except *:
|
||||
oov = '_' * self.chars_per_word
|
||||
cdef void set_featuresC(self, ExampleC* eg,
|
||||
const TokenC* tokens, object strings, const int i) except *:
|
||||
cdef unicode oov = ''
|
||||
p2 = strings[i-2] if i >= 2 else oov
|
||||
p1 = strings[i-1] if i >= 1 else oov
|
||||
w = strings[i]
|
||||
n1 = strings[i+1] if (i+1) < len(strings) else oov
|
||||
n2 = strings[i+2] if (i+2) < len(strings) else oov
|
||||
cdef int p = 0
|
||||
cdef int c
|
||||
cdef int chars_per_word = self.chars_per_word
|
||||
cdef unicode string
|
||||
eg.nr_feat = 0
|
||||
for string in (p2, p1, w, n1, n2):
|
||||
for c in range(chars_per_word / 2):
|
||||
eg.features[p].i = p
|
||||
eg.features[p].key = ord(string[c])
|
||||
eg.features[p].value = 1.0 if string[c] != u'_' else 0.0
|
||||
p += 1
|
||||
eg.features[p].i = p
|
||||
eg.features[p].key = ord(string[-(c+1)])
|
||||
eg.features[p].value = 1.0 if string[-(c+1)] != u'_' else 0.0
|
||||
p += 1
|
||||
eg.features[p].key = tokens[i-1].tag
|
||||
eg.features[p].value = 1.0
|
||||
eg.features[p].i = p
|
||||
p += 1
|
||||
eg.features[p].key = tokens[i-2].tag
|
||||
eg.features[p].value = 1.0
|
||||
eg.features[p].i = p
|
||||
eg.nr_feat = p+1
|
||||
set_character_features(&eg.features[eg.nr_feat],
|
||||
string, chars_per_word)
|
||||
eg.nr_feat += chars_per_word
|
||||
cdef int hist
|
||||
for hist in range(1, self.tags_window+1):
|
||||
tag = tokens[i-hist].tag if hist <= i else 0
|
||||
eg.features[eg.nr_feat].key = tag
|
||||
eg.features[eg.nr_feat].value = 1.0
|
||||
eg.nr_feat += 1
|
||||
cdef int p
|
||||
for p in range(eg.nr_feat):
|
||||
eg.features[p].i = p
|
||||
|
||||
def end_training(self):
|
||||
pass
|
||||
|
|
@ -179,23 +190,8 @@ cdef class CharacterTagger(NeuralNet):
|
|||
|
||||
property nr_feat:
|
||||
def __get__(self):
|
||||
return self.chars_per_word * 5 + 2
|
||||
|
||||
property chars_per_word:
|
||||
def __get__(self):
|
||||
return 16
|
||||
|
||||
|
||||
def _pad(word, nr_char):
|
||||
if len(word) == nr_char:
|
||||
pass
|
||||
elif len(word) > nr_char:
|
||||
split = nr_char / 2
|
||||
word = word[:split] + word[-split:]
|
||||
else:
|
||||
word = word.ljust(nr_char, '_')
|
||||
assert len(word) == nr_char, repr(word)
|
||||
return word
|
||||
nr_word = self.left_window + self.right_window + 1
|
||||
return self.chars_per_word * nr_word + self.tags_window
|
||||
|
||||
|
||||
cdef inline void _fill_from_token(atom_t* context, const TokenC* t) nogil:
|
||||
|
|
@ -293,9 +289,6 @@ cdef class Tagger:
|
|||
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):
|
||||
|
|
@ -319,14 +312,13 @@ cdef class Tagger:
|
|||
widths=self.model.widths,
|
||||
nr_class=self.vocab.morphology.n_tags,
|
||||
nr_feat=self.model.nr_feat)
|
||||
strings = [_pad(tok.text, self.model.chars_per_word) for tok in tokens]
|
||||
strings = [tok.text for tok in tokens]
|
||||
for i in range(tokens.length):
|
||||
eg.reset()
|
||||
if tokens.c[i].pos == 0:
|
||||
self.model.set_featuresC(&eg.c, tokens.c, strings, i)
|
||||
self.model.predict_example(eg)
|
||||
#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)
|
||||
tokens.is_tagged = True
|
||||
|
|
@ -345,7 +337,7 @@ cdef class Tagger:
|
|||
"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]
|
||||
strings = [_pad(tok.text, self.model.chars_per_word) for tok in tokens]
|
||||
strings = [tok.text for tok in tokens]
|
||||
cdef int correct = 0
|
||||
cdef Pool mem = Pool()
|
||||
cdef Example eg = Example(
|
||||
|
|
@ -357,16 +349,35 @@ cdef class Tagger:
|
|||
eg.reset()
|
||||
self.model.set_featuresC(&eg.c, tokens.c, strings, i)
|
||||
eg.costs = [golds[i] not in (j, -1) for j in range(eg.c.nr_class)]
|
||||
|
||||
self.model.train_example(eg)
|
||||
|
||||
#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
|
||||
tokens.is_tagged = True
|
||||
tokens._py_tokens = [None] * tokens.length
|
||||
return correct
|
||||
|
||||
|
||||
@cython.cdivision(True)
|
||||
cdef int set_character_features(FeatureC* feat, unicode string, int chars_per_word) except -1:
|
||||
cdef unicode oov = ''
|
||||
cdef int chars_per_side = min(chars_per_word / 2, len(string))
|
||||
# Fill from start
|
||||
cdef int c
|
||||
for c in range(chars_per_side):
|
||||
feat.key = ord(string[c])
|
||||
feat.value = 1.0
|
||||
feat += 1
|
||||
# If word is too short, zero this part of the array
|
||||
for c in range(chars_per_side, chars_per_word - chars_per_side):
|
||||
feat.key = 0
|
||||
feat.value = 0
|
||||
feat += 1
|
||||
# Fill suffix
|
||||
for c in range(chars_per_side):
|
||||
feat.key = ord(string[-(c+1)])
|
||||
feat.value = 1.0
|
||||
feat += 1
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user