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input_pipeline.py
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input_pipeline.py
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# Copyright 2020 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Input pipeline for the sequence tagging dataset."""
import codecs
import collections
import enum
import tensorflow.compat.v2 as tf
# Values for padding, unknown words and a root.
PAD = '<p>'
PAD_ID = 0
UNKNOWN = '<u>'
UNKNOWN_ID = 1
ROOT = '<r>'
ROOT_ID = 2
class CoNLLAttributes(enum.Enum):
"""CoNLL attributre names and indices.
A UD CoNLL file looks like:
1 They they PRON PRP Case=Nom|Number=Plur 2 nsubj
2 buy buy VERB VBP Number=Plur|PTense=Pres 0 root
3 books book NOUN NNS Number=Plur 2 obj
4 . . PUNCT . _ 2 punct
For details, please see: http://universaldependencies.org/format.html.
"""
ID = 0
FORM = 1
LEMMA = 2
UPOS = 3
XPOS = 4
FEATS = 5
HEAD = 6
DEPREL = 7
def create_vocabs(filename, max_num_forms=100000):
"""Loads corpus and create vocabulary lists.
Args:
filename: file name of a corpus.
max_num_forms: maximum number of tokens included.
Returns:
Dictionary containing named vocab dictionaries.
"""
form_counter = collections.Counter()
xpos_counter = collections.Counter()
with tf.io.gfile.GFile(filename, 'rb') as f:
for line in codecs.getreader('utf-8')(f):
line = line.strip()
split = line.split(u'\t')
if not line.startswith('#') and split[0]:
form_counter[split[CoNLLAttributes.FORM.value]] += 1
xpos_counter[split[CoNLLAttributes.XPOS.value]] += 1
special_tokens = {PAD: PAD_ID, UNKNOWN: UNKNOWN_ID, ROOT: ROOT_ID}
# create word form vocab
vocabs = {'forms': {}, 'xpos': {}}
vocabs['forms'].update(special_tokens)
vocabs['forms'].update({
form[0]: id for id, form in enumerate(
form_counter.most_common(max_num_forms), start=ROOT_ID + 1)
})
# create xpos vocab
vocabs['xpos'].update(special_tokens)
vocabs['xpos'].update({
tag[0]: id
for id, tag in enumerate(xpos_counter.most_common(), start=ROOT_ID + 1)
})
return vocabs
def create_token(token, attributes, vocabs):
"""Map for a token a selected subset of attributes to indices.
Input example: CoNLL 09 representation for a token.
['Ms.', 'ms.', 'ms.', 'NNP', '_', '2', 'TITLE]
Output example: Indices as defined in self._attributes, e.g., [word form,
part-of-speech tag, and head].
[1025, 3, 1]
Args:
token: CoNLL token atrributes.
attributes: selected attributes.
vocabs: dictonery of vocabs.
Returns:
List of attribute ids for a token, e.g. [1025, 3] with word id and pos id.
Raises:
ValueError: CoNLL attribute requested but not covered by mapping.
"""
selected_attributes = []
for attribute in attributes:
index = attribute.value
if attribute == CoNLLAttributes.FORM:
selected_attributes.append(vocabs['forms'].get(token[index], UNKNOWN_ID))
elif attribute == CoNLLAttributes.XPOS:
selected_attributes.append(vocabs['xpos'].get(token[index], UNKNOWN_ID))
elif attribute == CoNLLAttributes.HEAD:
selected_attributes.append(int(token[index]))
else:
raise ValueError('CoNLL index %s not covered by mapping.' %
str(attribute.name))
return selected_attributes
def create_sentence_with_root(attributes, vocabs):
"""Create a sentence containing a root.
Args:
attributes: attributes extracted from token.
vocabs: dictonery of vocabs.
Returns:
A list representing a sentence containing the root only,
e.g., [[2, 1, 0]] for root word, unknown xpos, and head 0.
"""
# Create the token properties of an artifical root node.
token_properties = [ROOT for _ in range(12)] # CoNLL 09 has 12 columns.
token_properties[CoNLLAttributes.ID.value] = '0'
token_properties[CoNLLAttributes.HEAD.value] = '0'
token = create_token(token_properties, attributes, vocabs)
if len(token) == 1:
token = token[0]
return [token]
def sentences_from_conll_data(corpus_filename,
vocabs,
attributes,
max_sentence_length=1000):
"""Load and returns conll data in list format.
Args:
corpus_filename: filename of corpus.
vocabs: dictionary of vocabs
attributes: list of conll attributes to include into the batch
max_sentence_length: cut off sentences longer as max tokens
Yields:
A sentence as a list of tokens while tokens are lists of attributes.
"""
with tf.io.gfile.GFile(corpus_filename, 'rb') as f:
sentence = create_sentence_with_root(attributes, vocabs)
for line in codecs.getreader('utf-8')(f):
line = line.strip()
if line.startswith('#'):
continue
split = line.split('\t')
if split[0]: # Not an empty line, process next token:
if len(sentence) < max_sentence_length:
if len(attributes) == 1:
sentence.append(create_token(split, attributes, vocabs)[0])
else:
sentence.append(create_token(split, attributes, vocabs))
else: # Sentences start with an empty line, yield sentence:
yield sentence
# Reset sentence.
sentence = create_sentence_with_root(attributes, vocabs)
if len(sentence) > 1: # sentences does not only contain a root.
yield sentence
def sentence_dataset_dict(filename,
vocabs,
attributes_input,
attributes_target,
batch_size,
bucket_size,
repeat=None,
prefetch_size=tf.data.experimental.AUTOTUNE):
"""Combines sentences into a dataset of padded batches.
Args:
filename: file name of a corpus.
vocabs: dictionary of dictionaries to map from strings to ids.
attributes_input: attributes for the input.
attributes_target: target attributes empty targets is not inclueded.
batch_size: the size of a batch.
bucket_size: the size of a bucket.
repeat: number of times the dataset is repeated.
prefetch_size: prefetch size of the data.
Returns:
Returns dataset as dictionary containing the data as key value pairs.
"""
data_keys = ['inputs']
if attributes_target:
data_keys.append('targets')
def generator():
"""Generator to create the data."""
input_generator = sentences_from_conll_data(
filename, vocabs, attributes_input, max_sentence_length=bucket_size)
if attributes_target:
target_generator = sentences_from_conll_data(
filename, vocabs, attributes_target, max_sentence_length=bucket_size)
for inputs in input_generator:
data = {'inputs': inputs}
if attributes_target:
data['targets'] = next(target_generator)
yield data
output_types = {k: tf.float32 for k in data_keys}
output_shapes = {k: (None,) for k in data_keys}
dataset = tf.data.Dataset.from_generator(
generator, output_types=output_types, output_shapes=output_shapes)
# cache the dataset in memory and repeat.
dataset = dataset.cache()
dataset = dataset.repeat(repeat)
# static padding up to bucket size.
padded_shapes = {k: [bucket_size] for k in data_keys}
dataset = dataset.padded_batch(
batch_size=batch_size, padded_shapes=(padded_shapes))
dataset = dataset.prefetch(prefetch_size)
return dataset