spaCy/spacy/tagger.pyx

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# cython: profile=True
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
from os import path
import os
import shutil
import random
import json
import cython
from .context cimport fill_context
from .context cimport N_FIELDS
from thinc.features cimport ConjFeat
NULL_TAG = 0
def setup_model_dir(tag_type, tag_names, templates, model_dir):
if path.exists(model_dir):
shutil.rmtree(model_dir)
os.mkdir(model_dir)
config = {
'tag_type': tag_type,
'templates': templates,
'tag_names': tag_names,
}
with open(path.join(model_dir, 'config.json'), 'w') as file_:
json.dump(config, file_)
def train(train_sents, model_dir, nr_iter=10):
cdef Tokens tokens
tagger = Tagger(model_dir)
for _ in range(nr_iter):
n_corr = 0
total = 0
for tokens, golds in train_sents:
assert len(tokens) == len(golds), [t.string for t in tokens]
for i in range(tokens.length):
if tagger.tag_type == POS:
gold = _get_gold_pos(i, golds, tokens.pos)
elif tagger.tag_type == ENTITY:
gold = _get_gold_ner(i, golds, tokens.ner)
guess = tagger.predict(i, tokens)
tokens.set_tag(i, tagger.tag_type, guess)
if gold is not None:
tagger.tell_answer(gold)
total += 1
n_corr += guess in gold
#print('%s\t%d\t%d' % (tokens[i].string, guess, gold))
print('%.4f' % ((n_corr / total) * 100))
random.shuffle(train_sents)
tagger.model.end_training()
tagger.model.dump(path.join(model_dir, 'model'))
cdef object _get_gold_pos(i, golds, int* pred):
if golds[i] == 0:
return None
else:
return [golds[i]]
cdef object _get_gold_ner(i, golds, int* ner):
if golds[i] == 0:
return None
else:
return [golds[i]]
def evaluate(tagger, sents):
n_corr = 0
total = 0
for tokens, golds in sents:
for i, gold in enumerate(golds):
guess = tagger.predict(i, tokens)
tokens.set_tag(i, tagger.tag_type, guess)
if gold != NULL_TAG:
total += 1
n_corr += guess == gold
return n_corr / total
cdef class Tagger:
"""Assign part-of-speech, named entity or supersense tags, using greedy
decoding. The tagger reads its model and configuration from disk.
"""
def __init__(self, model_dir):
self.mem = Pool()
cfg = json.load(open(path.join(model_dir, 'config.json')))
templates = cfg['templates']
self.tag_names = cfg['tag_names']
self.tag_type = cfg['tag_type']
self.extractor = Extractor(templates, [ConjFeat] * len(templates))
self.model = LinearModel(len(self.tag_names))
if path.exists(path.join(model_dir, 'model')):
self.model.load(path.join(model_dir, 'model'))
self._context = <atom_t*>self.mem.alloc(N_FIELDS, sizeof(atom_t))
self._feats = <feat_t*>self.mem.alloc(self.extractor.n+1, sizeof(feat_t))
self._values = <weight_t*>self.mem.alloc(self.extractor.n+1, sizeof(weight_t))
self._scores = <weight_t*>self.mem.alloc(self.model.nr_class, sizeof(weight_t))
self._guess = NULL_TAG
cpdef int set_tags(self, Tokens tokens) except -1:
"""Assign tags to a Tokens object.
>>> tokens = EN.tokenize(u'An example sentence.')
>>> assert tokens[0].pos == 'NO_TAG'
>>> EN.pos_tagger.set_tags(tokens)
>>> assert tokens[0].pos == 'DT'
"""
cdef int i
for i in range(tokens.length):
tokens.set_tag(i, self.tag_type, self.predict(i, tokens))
cpdef class_t predict(self, int i, Tokens tokens) except 0:
"""Predict the tag of tokens[i]. The tagger remembers the features and
prediction, in case you later call tell_answer.
>>> tokens = EN.tokenize(u'An example sentence.')
>>> tag = EN.pos_tagger.predict(0, tokens)
>>> assert tag == EN.pos_tagger.tag_id('DT') == 5
"""
fill_context(self._context, i, tokens)
self.extractor.extract(self._feats, self._values, self._context, NULL)
self._guess = self.model.score(self._scores, self._feats, self._values)
return self._guess
cpdef int tell_answer(self, list golds) except -1:
"""Provide the correct tag for the word the tagger was last asked to predict.
During Tagger.predict, the tagger remembers the features and prediction
for the example. These are used to calculate a weight update given the
correct label.
>>> tokens = EN.tokenize('An example sentence.')
>>> guess = EN.pos_tagger.predict(1, tokens)
>>> JJ = EN.pos_tagger.tag_id('JJ')
>>> JJ
7
>>> EN.pos_tagger.tell_answer(JJ)
"""
cdef class_t guess = self._guess
if guess in golds:
self.model.update({})
return 0
best_gold = golds[0]
best_score = self._scores[best_gold-1]
for gold in golds[1:]:
if self._scores[gold-1] > best_gold:
best_score = self._scores[best_gold-1]
best_gold = gold
counts = {guess: {}, best_gold: {}}
self.extractor.count(counts[best_gold], self._feats, 1)
self.extractor.count(counts[guess], self._feats, -1)
self.model.update(counts)
def tag_id(self, object tag_name):
"""Encode tag_name into a tag ID integer."""
tag_id = self.tag_names.index(tag_name)
if tag_id == -1:
tag_id = len(self.tag_names)
self.tag_names.append(tag_name)
return tag_id