def train(Language,
gold_tuples,
model_dir,
n_iter=15,
feat_set=u'basic',
seed=0,
gold_preproc=False,
force_gold=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
Config.write(dep_model_dir,
'config',
features=feat_set,
seed=seed,
labels=ArcEager.get_labels(gold_tuples))
nlp = Language(data_dir=model_dir,
tagger=False,
parser=False,
entity=False)
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for _, sents in gold_tuples:
for annot_tuples, _ in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, None, annot_tuples, verbose=False)
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
if not gold.is_projective:
raise Exception(
"Non-projective sentence in training, after we should "
"have enforced projectivity: %s" % annot_tuples)
loss += nlp.parser.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
print('%d:\t%d\t%.3f\t%.3f\t%.3f' %
(itn, loss, scorer.uas, scorer.tags_acc, scorer.token_acc))
print('end training')
nlp.end_training(model_dir)
print('done')
def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False, n_sents=0):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
ner_model_dir = path.join(model_dir, 'ner')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
os.mkdir(ner_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
gold_tuples = read_docparse_file(train_loc)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=Language.ParserTransitionSystem.get_labels(gold_tuples))
Config.write(ner_model_dir, 'config', features='ner', seed=seed,
labels=Language.EntityTransitionSystem.get_labels(gold_tuples))
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir)
print "Itn.\tUAS\tNER F.\tTag %"
for itn in range(n_iter):
scorer = Scorer()
for raw_text, segmented_text, annot_tuples in gold_tuples:
# Eval before train
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=False)
if gold_preproc:
sents = [nlp.tokenizer.tokens_from_list(s) for s in segmented_text]
else:
sents = [nlp.tokenizer(raw_text)]
for tokens in sents:
gold = GoldParse(tokens, annot_tuples)
nlp.tagger(tokens)
nlp.parser.train(tokens, gold)
if gold.ents:
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
print '%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.ents_f, scorer.tags_acc)
random.shuffle(gold_tuples)
nlp.parser.model.end_training()
nlp.entity.model.end_training()
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
def setup_model_dir(model_dir, labels, templates, feat_set='basic', seed=0):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=labels)
def train(Language,
sentences,
model_dir,
n_iter=15,
feat_set=u'basic',
seed=0,
gold_preproc=False,
force_gold=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir)
Config.write(dep_model_dir,
'config',
features=feat_set,
seed=seed,
labels=Language.ParserTransitionSystem.get_labels(sentences))
nlp = Language(data_dir=model_dir)
for itn in range(n_iter):
scorer = Scorer()
for _1, _2, (_3, words, tags, heads, labels, ner) in sentences:
tokens = nlp.tokenizer.tokens_from_list(words)
assert len(words) == len(tokens) == len(heads)
string_indices = [token.idx for token in tokens]
heads = [string_indices[head] for head in heads]
annot_tuples = (string_indices, words, tags, heads, labels, ner)
nlp.tagger.tag_from_strings(tokens, tags)
# Eval before train
nlp.parser(tokens)
scorer.score(tokens,
GoldParse(tokens, annot_tuples),
verbose=False)
# Make fresh tokens, and train
tokens = nlp.tokenizer.tokens_from_list(words)
nlp.tagger.tag_from_strings(tokens, tags)
try:
nlp.parser.train(tokens, GoldParse(tokens, annot_tuples))
except AssertionError:
continue
print '%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las)
random.shuffle(sentences)
nlp.parser.model.end_training()
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
def train(Language,
paragraphs,
model_dir,
n_iter=15,
feat_set=u'basic',
seed=0,
gold_preproc=False,
force_gold=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir)
left_labels, right_labels = get_labels(paragraphs)
Config.write(dep_model_dir,
'config',
features=feat_set,
seed=seed,
left_labels=left_labels,
right_labels=right_labels)
nlp = Language()
for itn in range(n_iter):
heads_corr = 0
pos_corr = 0
n_tokens = 0
for tokens, tag_strs, heads, labels in iter_data(
paragraphs, nlp.tokenizer, gold_preproc=gold_preproc):
nlp.tagger(tokens)
try:
heads_corr += nlp.parser.train_sent(tokens,
heads,
labels,
force_gold=force_gold)
except OracleError:
continue
pos_corr += nlp.tagger.train(tokens, tag_strs)
n_tokens += len(tokens)
acc = float(heads_corr) / n_tokens
pos_acc = float(pos_corr) / n_tokens
print '%d: ' % itn, '%.3f' % acc, '%.3f' % pos_acc
random.shuffle(paragraphs)
nlp.parser.model.end_training()
nlp.tagger.model.end_training()
return acc
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False, force_gold=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=ArcEager.get_labels(gold_tuples))
nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for _, sents in gold_tuples:
for annot_tuples, _ in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, None, annot_tuples, verbose=False)
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
if not gold.is_projective:
raise Exception(
"Non-projective sentence in training, after we should "
"have enforced projectivity: %s" % annot_tuples
)
loss += nlp.parser.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
print('%d:\t%d\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas,
scorer.tags_acc, scorer.token_acc))
print('end training')
nlp.end_training(model_dir)
print('done')
def from_dir(cls, tag_map, model_dir):
vocab = Vocab(tag_map=tag_map, get_lex_attr=Language.default_lex_attrs())
tokenizer = Tokenizer(vocab, {}, None, None, None)
tagger = Tagger.blank(vocab, TAGGER_TEMPLATES)
cfg = Config.read(path.join(model_dir, 'deps'), 'config')
parser = Parser.from_dir(path.join(model_dir, 'deps'), vocab.strings, ArcEager)
return cls(vocab, tokenizer, tagger, parser)
def from_dir(cls, tag_map, model_dir):
vocab = Vocab(tag_map=tag_map,
get_lex_attr=Language.default_lex_attrs())
tokenizer = Tokenizer(vocab, {}, None, None, None)
tagger = Tagger.blank(vocab, TAGGER_TEMPLATES)
cfg = Config.read(path.join(model_dir, 'deps'), 'config')
parser = Parser.from_dir(path.join(model_dir, 'deps'), vocab.strings,
ArcEager)
return cls(vocab, tokenizer, tagger, parser)
def setup_model_dir(model_dir,
labels,
templates,
feat_set='basic',
seed=0):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
Config.write(dep_model_dir,
'config',
features=feat_set,
seed=seed,
labels=labels)
def train(Language, sentences, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False, force_gold=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=Language.ParserTransitionSystem.get_labels(sentences))
nlp = Language(data_dir=model_dir)
for itn in range(n_iter):
scorer = Scorer()
for _1, _2, (_3, words, tags, heads, labels, ner) in sentences:
tokens = nlp.tokenizer.tokens_from_list(words)
assert len(words) == len(tokens) == len(heads)
string_indices = [token.idx for token in tokens]
heads = [string_indices[head] for head in heads]
annot_tuples = (string_indices, words, tags, heads, labels, ner)
nlp.tagger.tag_from_strings(tokens, tags)
# Eval before train
nlp.parser(tokens)
scorer.score(tokens, GoldParse(tokens, annot_tuples), verbose=False)
# Make fresh tokens, and train
tokens = nlp.tokenizer.tokens_from_list(words)
nlp.tagger.tag_from_strings(tokens, tags)
try:
nlp.parser.train(tokens, GoldParse(tokens, annot_tuples))
except AssertionError:
continue
print '%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las)
random.shuffle(sentences)
nlp.parser.model.end_training()
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
def train(Language, sents, model_dir, n_iter=15, feat_set=u'basic', seed=0):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir)
left_labels, right_labels = get_labels(sents)
Config.write(dep_model_dir,
'config',
features=feat_set,
seed=seed,
left_labels=left_labels,
right_labels=right_labels)
nlp = Language()
for itn in range(n_iter):
heads_corr = 0
pos_corr = 0
n_tokens = 0
for words, heads, labels, tags in sents:
tags = [nlp.tagger.tag_names.index(tag) for tag in tags]
tokens = nlp.tokenizer.tokens_from_list(words)
nlp.tagger(tokens)
heads_corr += nlp.parser.train_sent(tokens, heads, labels)
pos_corr += nlp.tagger.train(tokens, tags)
n_tokens += len(tokens)
acc = float(heads_corr) / n_tokens
pos_acc = float(pos_corr) / n_tokens
print '%d: ' % itn, '%.3f' % acc, '%.3f' % pos_acc
random.shuffle(sents)
nlp.parser.model.end_training()
nlp.tagger.model.end_training()
#nlp.parser.model.dump(path.join(dep_model_dir, 'model'), freq_thresh=0)
return acc
def train(Language, paragraphs, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False, force_gold=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir)
left_labels, right_labels = get_labels(paragraphs)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
left_labels=left_labels, right_labels=right_labels)
nlp = Language()
for itn in range(n_iter):
heads_corr = 0
pos_corr = 0
n_tokens = 0
for tokens, tag_strs, heads, labels in iter_data(paragraphs, nlp.tokenizer,
gold_preproc=gold_preproc):
nlp.tagger(tokens)
try:
heads_corr += nlp.parser.train_sent(tokens, heads, labels, force_gold=force_gold)
except OracleError:
continue
pos_corr += nlp.tagger.train(tokens, tag_strs)
n_tokens += len(tokens)
acc = float(heads_corr) / n_tokens
pos_acc = float(pos_corr) / n_tokens
print '%d: ' % itn, '%.3f' % acc, '%.3f' % pos_acc
random.shuffle(paragraphs)
nlp.parser.model.end_training()
nlp.tagger.model.end_training()
return acc
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
beam_width=1, verbose=False,
use_orig_arc_eager=False):
dep_model_dir = path.join(model_dir, 'deps')
ner_model_dir = path.join(model_dir, 'ner')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(ner_model_dir)
os.mkdir(pos_model_dir)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=ArcEager.get_labels(gold_tuples),
beam_width=beam_width)
Config.write(ner_model_dir, 'config', features='ner', seed=seed,
labels=BiluoPushDown.get_labels(gold_tuples),
beam_width=0)
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
nlp.entity = Parser.from_dir(ner_model_dir, nlp.vocab.strings, BiluoPushDown)
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for raw_text, sents in gold_tuples:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, raw_text, annot_tuples,
verbose=verbose if itn >= 2 else False)
if raw_text is None:
words = add_noise(annot_tuples[1], corruption_level)
tokens = nlp.tokenizer.tokens_from_list(words)
else:
raw_text = add_noise(raw_text, corruption_level)
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
if not gold.is_projective:
raise Exception(
"Non-projective sentence in training, after we should "
"have enforced projectivity: %s" % annot_tuples
)
loss += nlp.parser.train(tokens, gold)
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
scorer.tags_acc,
scorer.token_acc))
print('end training')
nlp.end_training(model_dir)
print('done')
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
beam_width=1, verbose=False,
use_orig_arc_eager=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
ner_model_dir = path.join(model_dir, 'ner')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
os.mkdir(ner_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=Language.ParserTransitionSystem.get_labels(gold_tuples),
beam_width=beam_width)
Config.write(ner_model_dir, 'config', features='ner', seed=seed,
labels=Language.EntityTransitionSystem.get_labels(gold_tuples),
beam_width=0)
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir)
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for raw_text, sents in gold_tuples:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, raw_text, annot_tuples,
verbose=verbose if itn >= 2 else False)
if raw_text is None:
words = add_noise(annot_tuples[1], corruption_level)
tokens = nlp.tokenizer.tokens_from_list(words)
else:
raw_text = add_noise(raw_text, corruption_level)
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
if not gold.is_projective:
raise Exception(
"Non-projective sentence in training, after we should "
"have enforced projectivity: %s" % annot_tuples
)
loss += nlp.parser.train(tokens, gold)
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
scorer.tags_acc,
scorer.token_acc))
nlp.end_training()
def train(Language,
gold_tuples,
model_dir,
n_iter=15,
feat_set=u'basic',
eta=0.01,
mu=0.9,
nv_hidden=100,
nv_word=10,
nv_tag=10,
nv_label=10,
seed=0,
n_sents=0,
verbose=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir)
Config.write(
dep_model_dir,
'config',
seed=seed,
templates=tuple(),
labels=Language.ParserTransitionSystem.get_labels(gold_tuples),
vector_lengths=(nv_word, nv_tag, nv_label),
hidden_nodes=nv_hidden,
eta=eta,
mu=mu)
# Bake-in hyper-parameters
optimizer = lambda loss, params: rms_prop(
loss, params, eta=eta, rho=rho, eps=eps)
nlp = Language(data_dir=model_dir)
n_classes = nlp.parser.model.n_classes
train, predict = compile_model(n_classes, nv_hidden, n_in, optimizer)
nlp.parser.model = TheanoModel(n_classes, input_spec, train, predict,
model_loc)
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
print "Itn.\tP.Loss\tUAS\tTag %\tToken %"
log_loc = path.join(model_dir, 'job.log')
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for _, sents in gold_tuples:
for annot_tuples, ctnt in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, annot_tuples)
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
assert gold.is_projective
loss += nlp.parser.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
logline = '%d:\t%d\t%.3f\t%.3f\t%.3f' % (
itn, loss, scorer.uas, scorer.tags_acc, scorer.token_acc)
print logline
with open(log_loc, 'aw') as file_:
file_.write(logline + '\n')
nlp.parser.model.end_training()
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
return nlp
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
beam_width=1, verbose=False,
use_orig_arc_eager=False, pseudoprojective=False):
dep_model_dir = path.join(model_dir, 'deps')
ner_model_dir = path.join(model_dir, 'ner')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(ner_model_dir)
os.mkdir(pos_model_dir)
if pseudoprojective:
# preprocess training data here before ArcEager.get_labels() is called
gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=ArcEager.get_labels(gold_tuples),
beam_width=beam_width,projectivize=pseudoprojective)
Config.write(ner_model_dir, 'config', features='ner', seed=seed,
labels=BiluoPushDown.get_labels(gold_tuples),
beam_width=0)
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
if nlp.lang == 'de':
nlp.vocab.morphology.lemmatizer = lambda string,pos: set([string])
nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
nlp.entity = Parser.from_dir(ner_model_dir, nlp.vocab.strings, BiluoPushDown)
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for raw_text, sents in gold_tuples:
if gold_preproc:
raw_text = None
else:
sents = _merge_sents(sents)
for annot_tuples, ctnt in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, raw_text, annot_tuples,
verbose=verbose if itn >= 2 else False)
if raw_text is None:
words = add_noise(annot_tuples[1], corruption_level)
tokens = nlp.tokenizer.tokens_from_list(words)
else:
raw_text = add_noise(raw_text, corruption_level)
tokens = nlp.tokenizer(raw_text)
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples)
if not gold.is_projective:
raise Exception("Non-projective sentence in training: %s" % annot_tuples[1])
loss += nlp.parser.train(tokens, gold)
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
scorer.tags_acc,
scorer.token_acc))
print('end training')
nlp.end_training(model_dir)
print('done')
def train(Language,
train_loc,
model_dir,
n_iter=15,
feat_set=u'basic',
seed=0,
gold_preproc=False,
n_sents=0):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
ner_model_dir = path.join(model_dir, 'ner')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
if path.exists(ner_model_dir):
shutil.rmtree(ner_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
os.mkdir(ner_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES,
pos_model_dir)
gold_tuples = read_docparse_file(train_loc)
Config.write(
dep_model_dir,
'config',
features=feat_set,
seed=seed,
labels=Language.ParserTransitionSystem.get_labels(gold_tuples))
Config.write(
ner_model_dir,
'config',
features='ner',
seed=seed,
labels=Language.EntityTransitionSystem.get_labels(gold_tuples))
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
nlp = Language(data_dir=model_dir)
print "Itn.\tUAS\tNER F.\tTag %"
for itn in range(n_iter):
scorer = Scorer()
for raw_text, segmented_text, annot_tuples in gold_tuples:
# Eval before train
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=False)
if gold_preproc:
sents = [
nlp.tokenizer.tokens_from_list(s) for s in segmented_text
]
else:
sents = [nlp.tokenizer(raw_text)]
for tokens in sents:
gold = GoldParse(tokens, annot_tuples)
nlp.tagger(tokens)
nlp.parser.train(tokens, gold)
if gold.ents:
nlp.entity.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
print '%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.ents_f,
scorer.tags_acc)
random.shuffle(gold_tuples)
nlp.parser.model.end_training()
nlp.entity.model.end_training()
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
eta=0.01, mu=0.9, nv_hidden=100, nv_word=10, nv_tag=10, nv_label=10,
seed=0, n_sents=0, verbose=False):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
if path.exists(dep_model_dir):
shutil.rmtree(dep_model_dir)
if path.exists(pos_model_dir):
shutil.rmtree(pos_model_dir)
os.mkdir(dep_model_dir)
os.mkdir(pos_model_dir)
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
Config.write(dep_model_dir, 'config',
seed=seed,
templates=tuple(),
labels=Language.ParserTransitionSystem.get_labels(gold_tuples),
vector_lengths=(nv_word, nv_tag, nv_label),
hidden_nodes=nv_hidden,
eta=eta,
mu=mu
)
# Bake-in hyper-parameters
optimizer = lambda loss, params: rms_prop(loss, params, eta=eta, rho=rho, eps=eps)
nlp = Language(data_dir=model_dir)
n_classes = nlp.parser.model.n_classes
train, predict = compile_model(n_classes, nv_hidden, n_in, optimizer)
nlp.parser.model = TheanoModel(n_classes, input_spec, train,
predict, model_loc)
if n_sents > 0:
gold_tuples = gold_tuples[:n_sents]
print "Itn.\tP.Loss\tUAS\tTag %\tToken %"
log_loc = path.join(model_dir, 'job.log')
for itn in range(n_iter):
scorer = Scorer()
loss = 0
for _, sents in gold_tuples:
for annot_tuples, ctnt in sents:
if len(annot_tuples[1]) == 1:
continue
score_model(scorer, nlp, annot_tuples)
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger(tokens)
gold = GoldParse(tokens, annot_tuples, make_projective=True)
assert gold.is_projective
loss += nlp.parser.train(tokens, gold)
nlp.tagger.train(tokens, gold.tags)
random.shuffle(gold_tuples)
logline = '%d:\t%d\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas,
scorer.tags_acc,
scorer.token_acc)
print logline
with open(log_loc, 'aw') as file_:
file_.write(logline + '\n')
nlp.parser.model.end_training()
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
return nlp