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https://github.com/explosion/spaCy.git
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* Refactor CharacterTagger
This commit is contained in:
Matthew Honnibal
parent
92e9134603
commit
fab538672e
131
spacy/tagger.pyx
131
spacy/tagger.pyx
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@ -1,4 +1,5 @@
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from __future__ import unicode_literals
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cimport cython
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import json
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from os import path
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from collections import defaultdict
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@ -10,6 +11,7 @@ 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 thinc.structs cimport FeatureC
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from .typedefs cimport attr_t
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from .tokens.doc cimport Doc
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@ -125,14 +127,29 @@ cdef class TaggerNeuralNet(NeuralNet):
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cdef class CharacterTagger(NeuralNet):
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def __init__(self, n_classes,
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depth=4, hidden_width=50,
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chars_width=10,
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words_width=20, shape_width=5, suffix_width=5, tags_width=20,
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learn_rate=0.1):
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input_length = 5 * chars_width * self.chars_per_word + 2 * tags_width
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widths = [input_length] + [hidden_width] * depth + [n_classes]
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depth=3, hidden_width=100, chars_width=5, tags_width=10, learn_rate=0.1,
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left_window=2, right_window=2, tags_window=10, chars_per_word=8):
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self.chars_per_word = chars_per_word
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self.chars_width = chars_width
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self.tags_width = tags_width
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self.left_window = left_window
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self.right_window = right_window
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self.tags_window = tags_window
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self.depth = depth
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self.hidden_width = hidden_width
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input_length = self.left_window * self.chars_width * self.chars_per_word \
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+ self.right_window * self.chars_width * self.chars_per_word \
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+ 1 * self.chars_width * self.chars_per_word \
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+ self.tags_window * self.tags_width
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widths = [input_length] + [self.hidden_width] * self.depth + [n_classes]
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vector_widths = [chars_width, tags_width]
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slots = [0] * 5 * self.chars_per_word + [1] * 2
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slots = [0] * self.left_window * self.chars_per_word \
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+ [0] * self.right_window * self.chars_per_word \
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+ [0] * 1 * self.chars_per_word \
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+ [1] * self.tags_window
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NeuralNet.__init__(
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self,
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widths,
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@ -141,36 +158,30 @@ cdef class CharacterTagger(NeuralNet):
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rho=1e-6,
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update_step='sgd')
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cdef void set_featuresC(self, ExampleC* eg, const TokenC* tokens, object strings, int i) except *:
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oov = '_' * self.chars_per_word
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cdef void set_featuresC(self, ExampleC* eg,
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const TokenC* tokens, object strings, const int i) except *:
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cdef unicode oov = ''
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p2 = strings[i-2] if i >= 2 else oov
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p1 = strings[i-1] if i >= 1 else oov
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w = strings[i]
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n1 = strings[i+1] if (i+1) < len(strings) else oov
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n2 = strings[i+2] if (i+2) < len(strings) else oov
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cdef int p = 0
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cdef int c
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cdef int chars_per_word = self.chars_per_word
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cdef unicode string
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eg.nr_feat = 0
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for string in (p2, p1, w, n1, n2):
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for c in range(chars_per_word / 2):
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eg.features[p].i = p
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eg.features[p].key = ord(string[c])
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eg.features[p].value = 1.0 if string[c] != u'_' else 0.0
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p += 1
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eg.features[p].i = p
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eg.features[p].key = ord(string[-(c+1)])
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eg.features[p].value = 1.0 if string[-(c+1)] != u'_' else 0.0
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p += 1
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eg.features[p].key = tokens[i-1].tag
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eg.features[p].value = 1.0
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eg.features[p].i = p
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p += 1
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eg.features[p].key = tokens[i-2].tag
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eg.features[p].value = 1.0
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eg.features[p].i = p
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eg.nr_feat = p+1
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set_character_features(&eg.features[eg.nr_feat],
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string, chars_per_word)
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eg.nr_feat += chars_per_word
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cdef int hist
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for hist in range(1, self.tags_window+1):
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tag = tokens[i-hist].tag if hist <= i else 0
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eg.features[eg.nr_feat].key = tag
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eg.features[eg.nr_feat].value = 1.0
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eg.nr_feat += 1
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cdef int p
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for p in range(eg.nr_feat):
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eg.features[p].i = p
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def end_training(self):
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pass
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@ -179,24 +190,9 @@ cdef class CharacterTagger(NeuralNet):
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property nr_feat:
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def __get__(self):
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return self.chars_per_word * 5 + 2
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nr_word = self.left_window + self.right_window + 1
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return self.chars_per_word * nr_word + self.tags_window
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property chars_per_word:
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def __get__(self):
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return 16
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def _pad(word, nr_char):
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if len(word) == nr_char:
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pass
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elif len(word) > nr_char:
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split = nr_char / 2
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word = word[:split] + word[-split:]
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else:
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word = word.ljust(nr_char, '_')
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assert len(word) == nr_char, repr(word)
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return word
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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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@ -293,9 +289,6 @@ cdef class Tagger:
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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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@ -319,14 +312,13 @@ cdef class Tagger:
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widths=self.model.widths,
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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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strings = [_pad(tok.text, self.model.chars_per_word) for tok in tokens]
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strings = [tok.text for tok in tokens]
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for i in range(tokens.length):
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eg.reset()
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if tokens.c[i].pos == 0:
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self.model.set_featuresC(&eg.c, tokens.c, strings, i)
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self.model.predict_example(eg)
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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(&tokens.c[i], guess)
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tokens.is_tagged = True
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@ -345,7 +337,7 @@ cdef class Tagger:
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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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strings = [_pad(tok.text, self.model.chars_per_word) for tok in tokens]
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strings = [tok.text for tok in tokens]
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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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@ -357,16 +349,35 @@ cdef class Tagger:
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eg.reset()
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self.model.set_featuresC(&eg.c, tokens.c, strings, i)
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eg.costs = [golds[i] not in (j, -1) for j in range(eg.c.nr_class)]
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self.model.train_example(eg)
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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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#
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#self.model.updateC(&eg.c)
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self.vocab.morphology.assign_tag(&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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tokens.is_tagged = True
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tokens._py_tokens = [None] * tokens.length
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return correct
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@cython.cdivision(True)
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cdef int set_character_features(FeatureC* feat, unicode string, int chars_per_word) except -1:
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cdef unicode oov = ''
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cdef int chars_per_side = min(chars_per_word / 2, len(string))
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# Fill from start
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cdef int c
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for c in range(chars_per_side):
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feat.key = ord(string[c])
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feat.value = 1.0
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feat += 1
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# If word is too short, zero this part of the array
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for c in range(chars_per_side, chars_per_word - chars_per_side):
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feat.key = 0
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feat.value = 0
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feat += 1
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# Fill suffix
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for c in range(chars_per_side):
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feat.key = ord(string[-(c+1)])
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feat.value = 1.0
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feat += 1
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