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This commit is contained in:
Matthew Honnibal 2016-07-27 02:56:36 +02:00
parent 6a98a3142f
commit ac63274e15
5 changed files with 55 additions and 221 deletions
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63
bin/parser/conll_train.py
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@ -163,26 +163,43 @@ def train(Language, gold_tuples, model_dir, dev_loc, n_iter=15, feat_set=u'basic
nr_trimmed = 0
eg_seen = 0
loss = 0
micro_eval = gold_tuples[:50]
for itn in range(n_iter):
random.shuffle(gold_tuples)
for _, sents in gold_tuples:
for annot_tuples, _ in sents:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger.tag_from_strings(tokens, annot_tuples[2])
gold = GoldParse(tokens, annot_tuples)
loss += nlp.parser.train(tokens, gold)
eg_seen += 1
if eg_seen % 10000 == 0:
dev_uas = score_file(nlp, dev_loc).uas
train_uas = score_sents(nlp, gold_tuples[:1000]).uas
size = nlp.parser.model.mem.size
print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%d' % (itn, int(loss), nr_trimmed,
train_uas, dev_uas, size))
loss = 0
nlp.end_training(model_dir)
try:
eg_seen = _train_epoch(nlp, gold_tuples, eg_seen, itn,
dev_loc, micro_eval)
except KeyboardInterrupt:
print("Saving model...")
break
#nlp.end_training(model_dir)
nlp.parser.model.end_training()
print("Saved. Evaluating...")
return nlp
def _train_epoch(nlp, gold_tuples, eg_seen, itn, dev_loc, micro_eval):
random.shuffle(gold_tuples)
loss = 0
nr_trimmed = 0
for _, sents in gold_tuples:
for annot_tuples, _ in sents:
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
nlp.tagger.tag_from_strings(tokens, annot_tuples[2])
gold = GoldParse(tokens, annot_tuples)
loss += nlp.parser.train(tokens, gold, itn=itn)
eg_seen += 1
if eg_seen % 1000 == 0:
if eg_seen % 20000 == 0:
dev_uas = score_file(nlp, dev_loc).uas
else:
dev_uas = 0.0
train_uas = score_sents(nlp, micro_eval).uas
size = nlp.parser.model.mem.size
nr_upd = nlp.parser.model.time
print('%d,%d:\t%d\t%.3f\t%.3f\t%.3f\t%d' % (itn, nr_upd, int(loss), nr_trimmed,
train_uas, dev_uas, size))
loss = 0
return eg_seen
@plac.annotations(
train_loc=("Location of CoNLL 09 formatted training file"),
@ -206,11 +223,13 @@ def main(train_loc, dev_loc, model_dir, n_iter=15, neural=False, batch_norm=Fals
batch_norm=batch_norm,
learn_rate=learn_rate,
update_step=update_step)
scorer = Scorer()
with io.open(dev_loc, 'r', encoding='utf8') as file_:
for _, sents in read_conll(file_):
for annot_tuples, _ in sents:
score_model(scorer, nlp, None, annot_tuples)
scorer = score_file(nlp, dev_loc)
#scorer = Scorer()
#with io.open(dev_loc, 'r', encoding='utf8') as file_:
# for _, sents in read_conll(file_):
# for annot_tuples, _ in sents:
# score_model(scorer, nlp, None, annot_tuples)
print('TOK', scorer.token_acc)
print('POS', scorer.tags_acc)
print('UAS', scorer.uas)

4
setup.py
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@ -50,6 +50,7 @@ MOD_NAMES = [
'spacy.syntax.stateclass',
'spacy.syntax._state',
'spacy.tokenizer',
'spacy.syntax._neural',
'spacy.syntax.parser',
'spacy.syntax.beam_parser',
'spacy.syntax.nonproj',
@ -174,7 +175,8 @@ def setup_package():
mod_path = mod_name.replace('.', '/') + '.cpp'
ext_modules.append(
Extension(mod_name, [mod_path],
language='c++', include_dirs=include_dirs))
language='c++', include_dirs=include_dirs,
libraries=['/Users/matt/blis/lib/blis']))
if not is_source_release(root):
generate_cython(root, 'spacy')

8
spacy/syntax/beam_parser.pyx
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@ -114,15 +114,13 @@ cdef class BeamParser(Parser):
else:
violn.check_crf(pred, gold)
if isinstance(self.model, ParserNeuralNet):
min_grad = 0.01 ** (itn+1)
min_grad = 0.1 ** (itn+1)
for grad, hist in zip(violn.p_probs, violn.p_hist):
assert not math.isnan(grad)
assert not math.isinf(grad)
assert not math.isnan(grad) and not math.isinf(grad)
if abs(grad) >= min_grad:
self._update_dense(tokens, hist, grad)
for grad, hist in zip(violn.g_probs, violn.g_hist):
assert not math.isnan(grad)
assert not math.isinf(grad)
assert not math.isnan(grad) and not math.isinf(grad)
if abs(grad) >= min_grad:
self._update_dense(tokens, hist, grad)
else:

32
spacy/syntax/parser.pxd
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@ -1,35 +1,11 @@
from thinc.linear.avgtron cimport AveragedPerceptron
from thinc.neural.nn cimport NeuralNet
from thinc.linear.features cimport ConjunctionExtracter
from thinc.base cimport Model
from thinc.extra.eg cimport Example
from thinc.typedefs cimport weight_t
from thinc.structs cimport FeatureC
from .stateclass cimport StateClass
from .arc_eager cimport TransitionSystem
from ..tokens.doc cimport Doc
from ..structs cimport TokenC
from thinc.structs cimport NeuralNetC, ExampleC
from ._state cimport StateC
from ..structs cimport TokenC
from thinc.base cimport Model
from thinc.linalg cimport *
cdef class ParserNeuralNet(NeuralNet):
cdef ConjunctionExtracter extracter
cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil
cdef class ParserPerceptron(AveragedPerceptron):
cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil
cdef class ParserNeuralNetEnsemble(ParserNeuralNet):
cdef object _models
cdef NeuralNetC** _models_c
cdef int** _masks
cdef int _nr_model
cdef class Parser:
cdef readonly Model model
cdef readonly TransitionSystem moves

169
spacy/syntax/parser.pyx
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@ -24,10 +24,13 @@ import random
from cymem.cymem cimport Pool, Address
from murmurhash.mrmr cimport hash64
from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t, idx_t
from thinc.linear.avgtron cimport AveragedPerceptron
from thinc.linalg cimport VecVec
from thinc.structs cimport NeuralNetC, SparseArrayC, ExampleC
from thinc.extra.eg cimport Example
from preshed.maps cimport MapStruct
from preshed.maps cimport map_get
from thinc.structs cimport FeatureC
@ -50,6 +53,7 @@ from ._parse_features cimport fill_context
from ._parse_features cimport *
from .stateclass cimport StateClass
from ._state cimport StateC
from ._neural cimport ParserNeuralNet, ParserPerceptron
DEBUG = False
@ -77,171 +81,6 @@ def ParserFactory(transition_system):
return lambda strings, dir_: Parser(strings, dir_, transition_system)
cdef class ParserPerceptron(AveragedPerceptron):
@property
def widths(self):
return (self.extracter.nr_templ,)
def update(self, Example eg):
'''Does regression on negative cost. Sort of cute?'''
self.time += 1
cdef weight_t loss = 0.0
best = eg.best
for clas in range(eg.c.nr_class):
if not eg.c.is_valid[clas]:
continue
if eg.c.scores[clas] < eg.c.scores[best]:
continue
loss += (-eg.c.costs[clas] - eg.c.scores[clas]) ** 2
d_loss = 2 * (-eg.c.costs[clas] - eg.c.scores[clas])
step = d_loss * 0.001
for feat in eg.c.features[:eg.c.nr_feat]:
self.update_weight(feat.key, clas, feat.value * step)
return int(loss)
cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil:
state = <const StateC*>_state
fill_context(eg.atoms, state)
eg.nr_feat = self.extracter.set_features(eg.features, eg.atoms)
cdef class ParserNeuralNet(NeuralNet):
def __init__(self, shape, **kwargs):
vector_widths = [4] * 76
slots = [0, 1, 2, 3] # S0
slots += [4, 5, 6, 7] # S1
slots += [8, 9, 10, 11] # S2
slots += [12, 13, 14, 15] # S3+
slots += [16, 17, 18, 19] # B0
slots += [20, 21, 22, 23] # B1
slots += [24, 25, 26, 27] # B2
slots += [28, 29, 30, 31] # B3+
slots += [32, 33, 34, 35] * 2 # S0l, S0r
slots += [36, 37, 38, 39] * 2 # B0l, B0r
slots += [40, 41, 42, 43] * 2 # S1l, S1r
slots += [44, 45, 46, 47] * 2 # S2l, S2r
slots += [48, 49, 50, 51, 52, 53, 54, 55]
slots += [53, 54, 55, 56]
input_length = sum(vector_widths[slot] for slot in slots)
widths = [input_length] + shape
NeuralNet.__init__(self, widths, embed=(vector_widths, slots), **kwargs)
@property
def nr_feat(self):
return 2000
cdef void set_featuresC(self, ExampleC* eg, const void* _state) nogil:
memset(eg.features, 0, 2000 * sizeof(FeatureC))
state = <const StateC*>_state
fill_context(eg.atoms, state)
feats = eg.features
feats = _add_token(feats, 0, state.S_(0), 1.0)
feats = _add_token(feats, 4, state.S_(1), 1.0)
feats = _add_token(feats, 8, state.S_(2), 1.0)
# Rest of the stack, with exponential decay
for i in range(3, state.stack_depth()):
feats = _add_token(feats, 12, state.S_(i), 1.0 * 0.5**(i-2))
feats = _add_token(feats, 16, state.B_(0), 1.0)
feats = _add_token(feats, 20, state.B_(1), 1.0)
feats = _add_token(feats, 24, state.B_(2), 1.0)
# Rest of the buffer, with exponential decay
for i in range(3, min(8, state.buffer_length())):
feats = _add_token(feats, 28, state.B_(i), 1.0 * 0.5**(i-2))
feats = _add_subtree(feats, 32, state, state.S(0))
feats = _add_subtree(feats, 40, state, state.B(0))
feats = _add_subtree(feats, 48, state, state.S(1))
feats = _add_subtree(feats, 56, state, state.S(2))
feats = _add_pos_bigram(feats, 64, state.S_(0), state.B_(0))
feats = _add_pos_bigram(feats, 65, state.S_(1), state.S_(0))
feats = _add_pos_bigram(feats, 66, state.S_(1), state.B_(0))
feats = _add_pos_bigram(feats, 67, state.S_(0), state.B_(1))
feats = _add_pos_bigram(feats, 68, state.S_(0), state.R_(state.S(0), 1))
feats = _add_pos_bigram(feats, 69, state.S_(0), state.R_(state.S(0), 2))
feats = _add_pos_bigram(feats, 70, state.S_(0), state.L_(state.S(0), 1))
feats = _add_pos_bigram(feats, 71, state.S_(0), state.L_(state.S(0), 2))
feats = _add_pos_trigram(feats, 72, state.S_(1), state.S_(0), state.B_(0))
feats = _add_pos_trigram(feats, 73, state.S_(0), state.B_(0), state.B_(1))
feats = _add_pos_trigram(feats, 74, state.S_(0), state.R_(state.S(0), 1),
state.R_(state.S(0), 2))
feats = _add_pos_trigram(feats, 75, state.S_(0), state.L_(state.S(0), 1),
state.L_(state.S(0), 2))
eg.nr_feat = feats - eg.features
cdef void _set_delta_lossC(self, weight_t* delta_loss,
const weight_t* Zs, const weight_t* scores) nogil:
for i in range(self.c.widths[self.c.nr_layer-1]):
delta_loss[i] = Zs[i]
cdef void _softmaxC(self, weight_t* out) nogil:
pass
cdef inline FeatureC* _add_token(FeatureC* feats,
int slot, const TokenC* token, weight_t value) nogil:
# Word
feats.i = slot
feats.key = token.lex.norm
feats.value = value
feats += 1
# POS tag
feats.i = slot+1
feats.key = token.tag
feats.value = value
feats += 1
# Dependency label
feats.i = slot+2
feats.key = token.dep
feats.value = value
feats += 1
# Word, label, tag
feats.i = slot+3
cdef uint64_t key[3]
key[0] = token.lex.cluster
key[1] = token.tag
key[2] = token.dep
feats.key = hash64(key, sizeof(key), 0)
feats.value = value
feats += 1
return feats
cdef inline FeatureC* _add_subtree(FeatureC* feats, int slot, const StateC* state, int t) nogil:
value = 1.0
for i in range(state.n_R(t)):
feats = _add_token(feats, slot, state.R_(t, i+1), value)
value *= 0.5
slot += 4
value = 1.0
for i in range(state.n_L(t)):
feats = _add_token(feats, slot, state.L_(t, i+1), value)
value *= 0.5
return feats
cdef inline FeatureC* _add_pos_bigram(FeatureC* feat, int slot,
const TokenC* t1, const TokenC* t2) nogil:
cdef uint64_t[2] key
key[0] = t1.tag
key[1] = t2.tag
feat.i = slot
feat.key = hash64(key, sizeof(key), slot)
feat.value = 1.0
return feat+1
cdef inline FeatureC* _add_pos_trigram(FeatureC* feat, int slot,
const TokenC* t1, const TokenC* t2, const TokenC* t3) nogil:
cdef uint64_t[3] key
key[0] = t1.tag
key[1] = t2.tag
key[2] = t3.tag
feat.i = slot
feat.key = hash64(key, sizeof(key), slot)
feat.value = 1.0
return feat+1
cdef class Parser:
def __init__(self, StringStore strings, transition_system, model):
self.moves = transition_system