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# Copyright 2018 The Google AI Language Team 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import csv
from . import tokenization
import tensorflow as tf
class InputExample(object):
def __init__(self, guid, text_a, cdr_number, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
CDR_number: string. The untokenized text of the CDR number
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.cdr_number = cdr_number
self.label = label
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
input_ids,
label_id,
cdr_number_ids,
is_real_example=True):
self.input_ids = input_ids
self.label_id = label_id
self.cdr_number_ids = cdr_number_ids
self.is_real_example = is_real_example
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_test_examples(self, data_dir):
"""Gets a collection of `InputExample`s for prediction."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with tf.io.gfile.GFile(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
lines.append(line)
return lines
class CDR_Ag_Processor(DataProcessor):
def get_examples(self, file_path):
"""See base class."""
return self._create_examples(
self._read_tsv(file_path))
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
set_type = tokenization.convert_to_unicode(line[0])
ID = tokenization.convert_to_unicode(line[1])
guid = "%s-%s" % (set_type, ID)
label = tokenization.convert_to_unicode(line[2])
text_a = tokenization.convert_to_unicode(line[3])
cdr_number = tokenization.convert_to_unicode(line[4])
examples.append(
InputExample(guid=guid, text_a=text_a, cdr_number=cdr_number, label=label))
return examples
def convert_single_example(ex_index, example, label_list, cdr_number_list , max_seq_length,
tokenizer):
"""Converts a single `InputExample` into a single `InputFeatures`."""
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
cdr_number_map = {}
for (i, CDR_number) in enumerate(cdr_number_list):
cdr_number_map[CDR_number] = i
tokens_a = tokenizer.tokenize(example.text_a)
if len(tokens_a) > max_seq_length:
tokens_a = tokens_a[0:max_seq_length]
tokens = []
cdr_number_ids = []
for (i, token) in enumerate(tokens_a):
tokens.append(token)
cdr_number_ids.append(cdr_number_map[example.cdr_number])
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
cdr_number_ids.append(0)
assert len(input_ids) == max_seq_length
assert len(cdr_number_ids) == max_seq_length
label_id = label_map[example.label]
if ex_index < 5:
tf.compat.v1.logging.info("*** Example ***")
tf.compat.v1.logging.info("guid: %s" % (example.guid))
tf.compat.v1.logging.info("tokens: %s" % " ".join(
[tokenization.printable_text(x) for x in tokens]))
tf.compat.v1.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.compat.v1.logging.info("label: %s (id = %d)" % (example.label, label_id))
tf.compat.v1.logging.info("CDR_number: %s" % " ".join([str(x) for x in cdr_number_ids]))
feature = InputFeatures(
input_ids=input_ids,
label_id=label_id,
cdr_number_ids=cdr_number_ids,
is_real_example=True)
return feature
def convert_examples_to_features(examples, label_list, cdr_number_list, max_seq_length,
tokenizer):
"""Convert a set of `InputExample`s to a list of `InputFeatures`."""
features = []
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
tf.compat.v1.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
feature = convert_single_example(ex_index, example, label_list, cdr_number_list,
max_seq_length, tokenizer)
features.append(feature)
return features
def main(_):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.INFO)
if __name__ == "__main__":
tf.compat.v1.app.run()
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