def test_load_careful(self):
config_data = {"labels": {"0": {"": True}, "1": {"": True}, "2": {"cc": True, "agent": True, "ccomp": True, "prt": True, "meta": True, "nsubjpass": True, "csubj": True, "conj": True, "dobj": True, "neg": True, "csubjpass": True, "mark": True, "auxpass": True, "advcl": True, "aux": True, "ROOT": True, "prep": True, "parataxis": True, "xcomp": True, "nsubj": True, "nummod": True, "advmod": True, "punct": True, "relcl": True, "quantmod": True, "acomp": True, "compound": True, "pcomp": True, "intj": True, "poss": True, "npadvmod": True, "case": True, "attr": True, "dep": True, "appos": True, "det": True, "nmod": True, "amod": True, "dative": True, "pobj": True, "expl": True, "predet": True, "preconj": True, "oprd": True, "acl": True}, "3": {"cc": True, "agent": True, "ccomp": True, "prt": True, "meta": True, "nsubjpass": True, "csubj": True, "conj": True, "acl": True, "poss": True, "neg": True, "mark": True, "auxpass": True, "advcl": True, "aux": True, "amod": True, "ROOT": True, "prep": True, "parataxis": True, "xcomp": True, "nsubj": True, "nummod": True, "advmod": True, "punct": True, "quantmod": True, "acomp": True, "pcomp": True, "intj": True, "relcl": True, "npadvmod": True, "case": True, "attr": True, "dep": True, "appos": True, "det": True, "nmod": True, "dobj": True, "dative": True, "pobj": True, "iobj": True, "expl": True, "predet": True, "preconj": True, "oprd": True}, "4": {"ROOT": True}}, "seed": 0, "features": "basic", "beam_width": 1}
data_dir = English.default_data_dir()
vocab = Vocab.from_dir(path.join(data_dir, 'vocab'))
moves = ArcEager(vocab.strings, config_data['labels'])
templates = get_templates(config_data['features'])
model = Model(moves.n_moves, templates, path.join(data_dir, 'deps'))
parser = Parser(vocab.strings, moves, model)
def main(train_loc, dev_loc, model_dir, tag_map_loc=None):
if tag_map_loc:
with open(tag_map_loc) as file_:
tag_map = json.loads(file_.read())
else:
tag_map = DEFAULT_TAG_MAP
train_sents = list(read_conllx(train_loc))
train_sents = PseudoProjectivity.preprocess_training_data(train_sents)
actions = ArcEager.get_actions(gold_parses=train_sents)
features = get_templates('basic')
model_dir = pathlib.Path(model_dir)
if not (model_dir / 'deps').exists():
(model_dir / 'deps').mkdir()
with (model_dir / 'deps' / 'config.json').open('wb') as file_:
file_.write(
json.dumps(
{'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8'))
vocab = Vocab(lex_attr_getters=Language.Defaults.lex_attr_getters, tag_map=tag_map)
# Populate vocab
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
for word in words:
_ = vocab[word]
for dep in deps:
_ = vocab[dep]
for tag in tags:
_ = vocab[tag]
if tag_map:
for tag in tags:
assert tag in tag_map, repr(tag)
tagger = Tagger(vocab, tag_map=tag_map)
parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0)
for itn in range(15):
loss = 0.
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
doc = Doc(vocab, words=words)
gold = GoldParse(doc, tags=tags, heads=heads, deps=deps)
tagger(doc)
loss += parser.update(doc, gold, itn=itn)
doc = Doc(vocab, words=words)
tagger.update(doc, gold)
random.shuffle(train_sents)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc))
nlp = Language(vocab=vocab, tagger=tagger, parser=parser)
nlp.end_training(model_dir)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(train_loc, dev_loc, model_dir, tag_map_loc):
with open(tag_map_loc) as file_:
tag_map = json.loads(file_.read())
train_sents = list(read_conllx(train_loc))
train_sents = PseudoProjectivity.preprocess_training_data(train_sents)
actions = ArcEager.get_actions(gold_parses=train_sents)
features = get_templates('basic')
model_dir = pathlib.Path(model_dir)
with (model_dir / 'deps' / 'config.json').open('w') as file_:
json.dump({'pseudoprojective': True, 'labels': actions, 'features': features}, file_)
vocab = Vocab(lex_attr_getters=Language.Defaults.lex_attr_getters, tag_map=tag_map)
# Populate vocab
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
for word in words:
_ = vocab[word]
for dep in deps:
_ = vocab[dep]
for tag in tags:
_ = vocab[tag]
for tag in tags:
assert tag in tag_map, repr(tag)
tagger = Tagger(vocab, tag_map=tag_map)
parser = DependencyParser(vocab, actions=actions, features=features)
for itn in range(15):
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
doc = Doc(vocab, words=words)
gold = GoldParse(doc, tags=tags, heads=heads, deps=deps)
tagger(doc)
parser.update(doc, gold)
doc = Doc(vocab, words=words)
tagger.update(doc, gold)
random.shuffle(train_sents)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc))
nlp = Language(vocab=vocab, tagger=tagger, parser=parser)
nlp.end_training(model_dir)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(train_loc, dev_loc, model_dir, tag_map_loc):
with open(tag_map_loc) as file_:
tag_map = json.loads(file_.read())
train_sents = list(read_conllx(train_loc))
labels = ArcEager.get_labels(train_sents)
templates = get_templates('basic')
TreebankParser.setup_model_dir(model_dir, labels, templates)
nlp = TreebankParser.from_dir(tag_map, model_dir)
for itn in range(15):
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
nlp.train(words, tags, heads, deps)
random.shuffle(train_sents)
scorer = score_model(nlp, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc))
nlp.end_training(model_dir)
scorer = score_model(nlp, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(train_loc, dev_loc, model_dir, tag_map_loc):
with open(tag_map_loc) as file_:
tag_map = json.loads(file_.read())
train_sents = list(read_conllx(train_loc))
labels = ArcEager.get_labels(train_sents)
templates = get_templates('basic')
TreebankParser.setup_model_dir(model_dir, labels, templates)
nlp = TreebankParser.from_dir(tag_map, model_dir)
for itn in range(15):
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
nlp.train(words, tags, heads, deps)
random.shuffle(train_sents)
scorer = score_model(nlp, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc))
nlp.end_training(model_dir)
scorer = score_model(nlp, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' %
(itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
LangClass = spacy.util.get_lang_class(lang_name)
train_sents = list(read_conllx(train_loc))
train_sents = PseudoProjectivity.preprocess_training_data(train_sents)
actions = ArcEager.get_actions(gold_parses=train_sents)
features = get_templates('basic')
model_dir = pathlib.Path(model_dir)
if not model_dir.exists():
model_dir.mkdir()
if not (model_dir / 'deps').exists():
(model_dir / 'deps').mkdir()
if not (model_dir / 'pos').exists():
(model_dir / 'pos').mkdir()
with (model_dir / 'deps' / 'config.json').open('wb') as file_:
file_.write(
json.dumps(
{'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8'))
vocab = LangClass.Defaults.create_vocab()
if not (model_dir / 'vocab').exists():
(model_dir / 'vocab').mkdir()
else:
if (model_dir / 'vocab' / 'strings.json').exists():
with (model_dir / 'vocab' / 'strings.json').open() as file_:
vocab.strings.load(file_)
if (model_dir / 'vocab' / 'lexemes.bin').exists():
vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin')
if clusters_loc is not None:
clusters_loc = pathlib.Path(clusters_loc)
with clusters_loc.open() as file_:
for line in file_:
try:
cluster, word, freq = line.split()
except ValueError:
continue
lex = vocab[word]
lex.cluster = int(cluster[::-1], 2)
# Populate vocab
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
for word in words:
_ = vocab[word]
for dep in deps:
_ = vocab[dep]
for tag in tags:
_ = vocab[tag]
if vocab.morphology.tag_map:
for tag in tags:
assert tag in vocab.morphology.tag_map, repr(tag)
tagger = Tagger(vocab)
parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0)
for itn in range(30):
loss = 0.
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
doc = Doc(vocab, words=words)
gold = GoldParse(doc, tags=tags, heads=heads, deps=deps)
tagger(doc)
loss += parser.update(doc, gold, itn=itn)
doc = Doc(vocab, words=words)
tagger.update(doc, gold)
random.shuffle(train_sents)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc))
nlp = LangClass(vocab=vocab, tagger=tagger, parser=parser)
nlp.end_training(model_dir)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
LangClass = spacy.util.get_lang_class(lang_name)
train_sents = list(read_conllx(train_loc))
train_sents = PseudoProjectivity.preprocess_training_data(train_sents)
actions = ArcEager.get_actions(gold_parses=train_sents)
features = get_templates('basic')
model_dir = pathlib.Path(model_dir)
if not model_dir.exists():
model_dir.mkdir()
if not (model_dir / 'deps').exists():
(model_dir / 'deps').mkdir()
if not (model_dir / 'pos').exists():
(model_dir / 'pos').mkdir()
with (model_dir / 'deps' / 'config.json').open('wb') as file_:
file_.write(
json.dumps(
{'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8'))
vocab = LangClass.Defaults.create_vocab()
if not (model_dir / 'vocab').exists():
(model_dir / 'vocab').mkdir()
else:
if (model_dir / 'vocab' / 'strings.json').exists():
with (model_dir / 'vocab' / 'strings.json').open() as file_:
vocab.strings.load(file_)
if (model_dir / 'vocab' / 'lexemes.bin').exists():
vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin')
if clusters_loc is not None:
clusters_loc = pathlib.Path(clusters_loc)
with clusters_loc.open() as file_:
for line in file_:
try:
cluster, word, freq = line.split()
except ValueError:
continue
lex = vocab[word]
lex.cluster = int(cluster[::-1], 2)
# Populate vocab
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
for word in words:
_ = vocab[word]
for dep in deps:
_ = vocab[dep]
for tag in tags:
_ = vocab[tag]
if vocab.morphology.tag_map:
for tag in tags:
assert tag in vocab.morphology.tag_map, repr(tag)
tagger = Tagger(vocab)
parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0)
for itn in range(30):
loss = 0.
for _, doc_sents in train_sents:
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
doc = Doc(vocab, words=words)
gold = GoldParse(doc, tags=tags, heads=heads, deps=deps)
tagger(doc)
loss += parser.update(doc, gold, itn=itn)
doc = Doc(vocab, words=words)
tagger.update(doc, gold)
random.shuffle(train_sents)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc))
nlp = Language(vocab=vocab, tagger=tagger, parser=parser)
nlp.end_training(model_dir)
scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def test_load_careful(self):
config_data = {
"labels": {
"0": {
"": True
},
"1": {
"": True
},
"2": {
"cc": True,
"agent": True,
"ccomp": True,
"prt": True,
"meta": True,
"nsubjpass": True,
"csubj": True,
"conj": True,
"dobj": True,
"neg": True,
"csubjpass": True,
"mark": True,
"auxpass": True,
"advcl": True,
"aux": True,
"ROOT": True,
"prep": True,
"parataxis": True,
"xcomp": True,
"nsubj": True,
"nummod": True,
"advmod": True,
"punct": True,
"relcl": True,
"quantmod": True,
"acomp": True,
"compound": True,
"pcomp": True,
"intj": True,
"poss": True,
"npadvmod": True,
"case": True,
"attr": True,
"dep": True,
"appos": True,
"det": True,
"nmod": True,
"amod": True,
"dative": True,
"pobj": True,
"expl": True,
"predet": True,
"preconj": True,
"oprd": True,
"acl": True
},
"3": {
"cc": True,
"agent": True,
"ccomp": True,
"prt": True,
"meta": True,
"nsubjpass": True,
"csubj": True,
"conj": True,
"acl": True,
"poss": True,
"neg": True,
"mark": True,
"auxpass": True,
"advcl": True,
"aux": True,
"amod": True,
"ROOT": True,
"prep": True,
"parataxis": True,
"xcomp": True,
"nsubj": True,
"nummod": True,
"advmod": True,
"punct": True,
"quantmod": True,
"acomp": True,
"pcomp": True,
"intj": True,
"relcl": True,
"npadvmod": True,
"case": True,
"attr": True,
"dep": True,
"appos": True,
"det": True,
"nmod": True,
"dobj": True,
"dative": True,
"pobj": True,
"iobj": True,
"expl": True,
"predet": True,
"preconj": True,
"oprd": True
},
"4": {
"ROOT": True
}
},
"seed": 0,
"features": "basic",
"beam_width": 1
}
data_dir = English.default_data_dir()
vocab = Vocab.from_dir(path.join(data_dir, 'vocab'))
moves = ArcEager(vocab.strings, config_data['labels'])
templates = get_templates(config_data['features'])
model = Model(moves.n_moves, templates, path.join(data_dir, 'deps'))
parser = Parser(vocab.strings, moves, model)
