class CoNLL_SDP_Transform(CoNLLTransform):
def __init__(self, config: SerializableDict = None, map_x=True, map_y=True, lower=True, n_buckets=32,
n_tokens_per_batch=5000, min_freq=2, **kwargs) -> None:
super().__init__(config, map_x, map_y, lower, n_buckets, n_tokens_per_batch, min_freq, **kwargs)
self.orphan_relation = ROOT
def lock_vocabs(self):
super().lock_vocabs()
# heuristic to find the orphan relation
for rel in self.rel_vocab.idx_to_token:
if 'root' in rel.lower():
self.orphan_relation = rel
break
def file_to_inputs(self, filepath: str, gold=True):
assert gold, 'only support gold file for now'
for i, sent in enumerate(read_conll(filepath)):
prev_cells = None
parsed_sent = []
heads = []
rels = []
for j, cell in enumerate(sent):
ID = cell[0]
form = cell[1]
cpos = cell[3]
head = cell[6]
deprel = cell[7]
if prev_cells and ID != prev_cells[0]: # found end of token
parsed_sent.append([prev_cells[1], prev_cells[2], heads, rels])
heads = []
rels = []
heads.append(head)
rels.append(deprel)
prev_cells = [ID, form, cpos, head, deprel]
parsed_sent.append([prev_cells[1], prev_cells[2], heads, rels])
yield parsed_sent
def fit(self, trn_path: str, **kwargs) -> int:
self.form_vocab = Vocab()
self.form_vocab.add(ROOT) # make root the 2ed elements while 0th is pad, 1st is unk
self.cpos_vocab = Vocab(pad_token=None, unk_token=None)
self.rel_vocab = Vocab(pad_token=None, unk_token=None)
num_samples = 0
counter = Counter()
for sent in self.file_to_samples(trn_path, gold=True):
num_samples += 1
for idx, (form, cpos, head, deprel) in enumerate(sent):
if idx == 0:
root = form
else:
counter[form] += 1
self.cpos_vocab.add(cpos)
self.rel_vocab.update(deprel)
for token in [token for token, freq in counter.items() if freq >= self.config.min_freq]:
self.form_vocab.add(token)
return num_samples
def inputs_to_samples(self, inputs, gold=False):
for sent in inputs:
sample = []
if self.config['lower']:
for i, cell in enumerate(sent):
cell = list(sent[i])
cell[0] = cell[0].lower()
if not gold:
cell += [[0], [self.rel_vocab.safe_pad_token]]
sample.append(cell)
# insert root word with arbitrary fields, anyway it will be masked
form, cpos, head, deprel = sample[0]
sample.insert(0, [self.bos, self.bos, [0], deprel])
yield sample
def samples_to_dataset(self, samples: Generator, map_x=None, map_y=None, batch_size=5000, shuffle=None, repeat=None,
drop_remainder=False, prefetch=1, cache=True) -> tf.data.Dataset:
def generator():
# custom bucketing, load corpus into memory
corpus = list(x for x in (samples() if callable(samples) else samples))
lengths = [1 + len(i) for i in corpus]
if len(corpus) < 32:
n_buckets = 1
else:
n_buckets = min(self.config.n_buckets, len(corpus))
buckets = dict(zip(*kmeans(lengths, n_buckets)))
sizes, buckets = zip(*[
(size, bucket) for size, bucket in buckets.items()
])
# the number of chunks in each bucket, which is clipped by
# range [1, len(bucket)]
chunks = [min(len(bucket), max(round(size * len(bucket) / batch_size), 1)) for size, bucket in
zip(sizes, buckets)]
range_fn = randperm if shuffle else arange
for i in tolist(range_fn(len(buckets))):
split_sizes = [(len(buckets[i]) - j - 1) // chunks[i] + 1
for j in range(chunks[i])]
for batch_indices in tf.split(range_fn(len(buckets[i])), split_sizes):
indices = [buckets[i][j] for j in tolist(batch_indices)]
raw_batch = [[], [], [], []]
max_len = len(max([corpus[i] for i in indices], key=len))
for idx in indices:
arc = np.zeros((max_len, max_len), dtype=np.bool)
rel = np.zeros((max_len, max_len), dtype=np.int64)
for b in raw_batch[:2]:
b.append([])
for m, cells in enumerate(corpus[idx]):
for b, c, v in zip(raw_batch, cells,
[self.form_vocab, self.cpos_vocab]):
b[-1].append(v.get_idx_without_add(c))
for n, r in zip(cells[2], cells[3]):
arc[m, n] = True
rid = self.rel_vocab.get_idx_without_add(r)
if rid is None:
logger.warning(f'Relation OOV: {r} not exists in train')
continue
rel[m, n] = rid
raw_batch[-2].append(arc)
raw_batch[-1].append(rel)
batch = []
for b, v in zip(raw_batch, [self.form_vocab, self.cpos_vocab]):
b = tf.keras.preprocessing.sequence.pad_sequences(b, padding='post',
value=v.safe_pad_token_idx,
dtype='int64')
batch.append(b)
batch += raw_batch[2:]
assert len(batch) == 4
yield (batch[0], batch[1]), (batch[2], batch[3])
# for x in generator():
# print(len(x[-1][-1]))
return super().samples_to_dataset(generator, False, False, 0, False, repeat, drop_remainder, prefetch,
cache)
def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
types = (tf.int64, tf.int64), (tf.bool, tf.int64)
shapes = ([None, None], [None, None]), ([None, None, None], [None, None, None])
values = (self.form_vocab.safe_pad_token_idx, self.cpos_vocab.safe_pad_token_idx), (
False, self.rel_vocab.safe_pad_token_idx)
return types, shapes, values
def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=False, inputs=None, X=None) -> Iterable:
arc_preds, rel_preds, mask = Y
sents = []
for arc_sent, rel_sent, length in zip(arc_preds, rel_preds,
tf.math.count_nonzero(mask, axis=-1)):
sent = []
for arc, rel in zip(tolist(arc_sent[1:, 1:]), tolist(rel_sent[1:, 1:])):
ar = []
for idx, (a, r) in enumerate(zip(arc, rel)):
if a:
ar.append((idx + 1, self.rel_vocab.idx_to_token[r]))
if not ar:
# orphan
ar.append((0, self.orphan_relation))
sent.append(ar)
sents.append(sent)
return sents
def XY_to_inputs_outputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]], Y: Union[tf.Tensor, Tuple[tf.Tensor]],
gold=False, inputs=None, conll=True) -> Iterable:
(words, feats, mask), (arc_preds, rel_preds) = X, Y
xs = inputs
ys = self.Y_to_outputs((arc_preds, rel_preds, mask))
sents = []
for x, y in zip(xs, ys):
sent = CoNLLSentence()
for idx, ((form, cpos), pred) in enumerate(zip(x, y)):
head = [p[0] for p in pred]
deprel = [p[1] for p in pred]
if conll:
sent.append(CoNLLWord(id=idx + 1, form=form, cpos=cpos, head=head, deprel=deprel))
else:
sent.append([head, deprel])
sents.append(sent)
return sents
class TransformerTransform(TsvTaggingFormat, Transform):
def __init__(self,
tokenizer=None,
config: SerializableDict = None,
map_x=False,
map_y=False,
**kwargs) -> None:
super().__init__(config, map_x, map_y, **kwargs)
self._tokenizer = tokenizer
self.tag_vocab: Vocab = None
self.special_token_ids = None
@property
def tokenizer(self):
return self._tokenizer
@tokenizer.setter
def tokenizer(self, tokenizer):
self._tokenizer = tokenizer
self.special_token_ids = tf.constant(
[tokenizer.vocab[token] for token in ['[PAD]', '[CLS]', '[SEP]']],
dtype=tf.int32)
def fit(self, trn_path: str, **kwargs) -> int:
self.tag_vocab = Vocab(unk_token=None)
num_samples = 0
for words, tags in self.file_to_inputs(trn_path, gold=True):
num_samples += 1
self.tag_vocab.update(tags)
return num_samples
def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
max_seq_length = self.config.get('max_seq_length', 128)
types = (tf.int32, tf.int32, tf.int32), tf.int32
# (input_ids, input_mask, segment_ids), label_ids
shapes = ([max_seq_length], [max_seq_length], [max_seq_length]), [None]
values = (0, 0, 0), self.tag_vocab.pad_idx
return types, shapes, values
def lock_vocabs(self):
super().lock_vocabs()
def inputs_to_samples(self, inputs, gold=False):
max_seq_length = self.config.get('max_seq_length', 128)
tokenizer = self._tokenizer
xlnet = False
roberta = False
pad_token = '[PAD]'
cls_token = '[CLS]'
sep_token = '[SEP]'
unk_token = '[UNK]'
pad_label_idx = self.tag_vocab.pad_idx
pad_token = tokenizer.convert_tokens_to_ids([pad_token])[0]
for sample in inputs:
if gold:
words, tags = sample
else:
words, tags = sample, [self.tag_vocab.pad_token] * len(sample)
input_ids, input_mask, segment_ids, label_ids = convert_examples_to_features(
words,
tags,
self.tag_vocab.token_to_idx,
max_seq_length,
tokenizer,
cls_token_at_end=xlnet,
# xlnet has a cls token at the end
cls_token=cls_token,
cls_token_segment_id=2 if xlnet else 0,
sep_token=sep_token,
sep_token_extra=roberta,
# roberta uses an extra separator b/w pairs of sentences, cf. github.com/pytorch/fairseq/commit/1684e166e3da03f5b600dbb7855cb98ddfcd0805
pad_on_left=xlnet,
# pad on the left for xlnet
pad_token=pad_token,
pad_token_segment_id=4 if xlnet else 0,
pad_token_label_id=pad_label_idx,
unk_token=unk_token)
if None in input_ids:
print(input_ids)
if None in input_mask:
print(input_mask)
if None in segment_ids:
print(input_mask)
yield (input_ids, input_mask, segment_ids), label_ids
def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
raise NotImplementedError(
'transformers has its own tagger, not need to convert idx for x')
def y_to_idx(self, y) -> tf.Tensor:
raise NotImplementedError(
'transformers has its own tagger, not need to convert idx for y')
def input_is_single_sample(
self, input: Union[List[str], List[List[str]]]) -> bool:
return isinstance(input[0], str)
def Y_to_outputs(self,
Y: Union[tf.Tensor, Tuple[tf.Tensor]],
gold=False,
X=None,
inputs=None,
**kwargs) -> Iterable:
assert X is not None, 'Need the X to know actual length of Y'
input_ids, input_mask, segment_ids = X
mask = tf.reduce_all(tf.not_equal(tf.expand_dims(input_ids, axis=-1),
self.special_token_ids),
axis=-1)
Y = tf.argmax(Y, axis=-1)
Y = Y[mask]
tags = [self.tag_vocab.idx_to_token[tid] for tid in Y]
offset = 0
for words in inputs:
yield tags[offset:offset + len(words)]
offset += len(words)
def __init__(self,
filepath: str = None,
vocab: Vocab = None,
expand_vocab=True,
lowercase=True,
input_dim=None,
output_dim=None,
unk=None,
normalize=False,
embeddings_initializer='VarianceScaling',
embeddings_regularizer=None,
activity_regularizer=None,
embeddings_constraint=None,
mask_zero=True,
input_length=None,
name=None,
**kwargs):
filepath = get_resource(filepath)
word2vec, _output_dim = load_word2vec(filepath)
if output_dim:
assert output_dim == _output_dim, f'output_dim = {output_dim} does not match {filepath}'
output_dim = _output_dim
# if the `unk` token exists in the pretrained,
# then replace it with a self-defined one, usually the one in word vocab
if unk and unk in word2vec:
word2vec[vocab.safe_unk_token] = word2vec.pop(unk)
if vocab is None:
vocab = Vocab()
vocab.update(word2vec.keys())
if expand_vocab and vocab.mutable:
for word in word2vec:
vocab.get_idx(word.lower() if lowercase else word)
if input_dim:
assert input_dim == len(
vocab), f'input_dim = {input_dim} does not match {filepath}'
input_dim = len(vocab)
# init matrix
self._embeddings_initializer = embeddings_initializer
embeddings_initializer = tf.keras.initializers.get(
embeddings_initializer)
with tf.device('cpu:0'):
pret_embs = embeddings_initializer(
shape=[input_dim, output_dim]).numpy()
# insert to pret_embs
for word, idx in vocab.token_to_idx.items():
vec = word2vec.get(word, None)
# Retry lower case
if vec is None and lowercase:
vec = word2vec.get(word.lower(), None)
if vec is not None:
pret_embs[idx] = vec
if normalize:
pret_embs /= np.std(pret_embs)
if not name:
name = os.path.splitext(os.path.basename(filepath))[0]
super().__init__(input_dim,
output_dim,
tf.keras.initializers.Constant(pret_embs),
embeddings_regularizer,
activity_regularizer,
embeddings_constraint,
mask_zero,
input_length,
name=name,
**kwargs)
self.filepath = filepath
self.expand_vocab = expand_vocab
self.lowercase = lowercase
class TSVTaggingTransform(TsvTaggingFormat, Transform):
def __init__(self,
config: SerializableDict = None,
map_x=True,
map_y=True,
use_char=False,
**kwargs) -> None:
super().__init__(**merge_locals_kwargs(locals(), kwargs))
self.word_vocab: Optional[Vocab] = None
self.tag_vocab: Optional[Vocab] = None
self.char_vocab: Optional[Vocab] = None
def fit(self, trn_path: str, **kwargs) -> int:
self.word_vocab = Vocab()
self.tag_vocab = Vocab(pad_token=None, unk_token=None)
num_samples = 0
for words, tags in self.file_to_inputs(trn_path, True):
self.word_vocab.update(words)
self.tag_vocab.update(tags)
num_samples += 1
if self.char_vocab:
self.char_vocab = Vocab()
for word in self.word_vocab.token_to_idx.keys():
if word in (self.word_vocab.pad_token,
self.word_vocab.unk_token):
continue
self.char_vocab.update(list(word))
return num_samples
def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
types = tf.string, tf.string
shapes = [None], [None]
values = self.word_vocab.pad_token, self.tag_vocab.first_token
return types, shapes, values
def inputs_to_samples(self, inputs, gold=False):
lower = self.config.get('lower', False)
if gold:
if lower:
for x, y in inputs:
yield x.lower(), y
else:
yield from inputs
else:
for x in inputs:
yield x.lower() if lower else x, [self.padding_values[-1]
] * len(x)
def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
return self.word_vocab.lookup(x)
def y_to_idx(self, y) -> tf.Tensor:
return self.tag_vocab.lookup(y)
def X_to_inputs(self, X: Union[tf.Tensor, Tuple[tf.Tensor]]) -> Iterable:
for xs in X:
words = []
for x in xs:
words.append(
str_tensor_to_str(x) if self.char_vocab else self.
word_vocab.idx_to_token[int(x)])
yield words
def Y_to_outputs(self,
Y: Union[tf.Tensor, Tuple[tf.Tensor]],
gold=False,
inputs=None,
X=None,
**kwargs) -> Iterable:
if not gold:
Y = tf.argmax(Y, axis=2)
for ys, xs in zip(Y, inputs):
tags = []
for y, x in zip(ys, xs):
tags.append(self.tag_vocab.idx_to_token[int(y)])
yield tags
def input_is_single_sample(
self, input: Union[List[str], List[List[str]]]) -> bool:
return isinstance(input[0], str)
def input_truth_output_to_str(self, input: List[str], truth: List[str],
output: List[str]):
text = ''
for word, gold_tag, pred_tag in zip(input, truth, output):
text += ' '.join([word, gold_tag, pred_tag]) + '\n'
text += '\n'
return text
class TextTransform(Transform):
def __init__(self,
forward=True,
seq_len=10,
tokenizer='char',
config: SerializableDict = None, map_x=True, map_y=True, **kwargs) -> None:
super().__init__(config, map_x, map_y, seq_len=seq_len, tokenizer=tokenizer, forward=forward, **kwargs)
self.vocab: Vocab = None
def tokenize_func(self):
if self.config.tokenizer == 'char':
return list
elif self.config.tokenizer == 'whitespace':
return lambda x: x.split()
else:
return lambda x: x.split(self.config.tokenizer)
def fit(self, trn_path: str, **kwargs) -> int:
self.vocab = Vocab()
num_samples = 0
for x, y in self.file_to_inputs(trn_path):
self.vocab.update(x)
num_samples += 1
return num_samples
def create_types_shapes_values(self) -> Tuple[Tuple, Tuple, Tuple]:
types = tf.string, tf.string
shapes = [None], [None]
defaults = self.vocab.pad_token, self.vocab.pad_token
return types, shapes, defaults
def file_to_inputs(self, filepath: str, gold=True):
forward = self.config.forward
seq_len = self.config.seq_len
buffer = []
tokenizer = self.tokenize_func()
with open(filepath, encoding='utf-8') if forward else FileReadBackwards(filepath, encoding="utf-8") as src:
for line in src:
tokens = tokenizer(line)
buffer += tokens
while len(buffer) > seq_len:
yield buffer[:seq_len], buffer[1:1 + seq_len]
buffer.pop(0)
def inputs_to_samples(self, inputs, gold=False):
forward = self.config.forward
for t in inputs:
if gold:
x, y = t
else:
x, y = t, t
if not forward:
x = list(reversed(x))
y = list(reversed(y))
yield x, y
def x_to_idx(self, x) -> Union[tf.Tensor, Tuple]:
return self.vocab.lookup(x)
def y_to_idx(self, y) -> tf.Tensor:
return self.x_to_idx(y)
def Y_to_outputs(self, Y: Union[tf.Tensor, Tuple[tf.Tensor]], gold=False, inputs=None, **kwargs) -> Iterable:
pred = tf.argmax(Y, axis=-1)
for ys, ms in zip(pred, inputs):
ret = []
for y in ys:
ret.append(self.vocab.idx_to_token[int(y)])
yield ret
def input_is_single_sample(self, input: Any) -> bool:
return isinstance(input[0], str)