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doccano.py
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doccano.py
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# coding=utf-8
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
import os
import time
import argparse
import json
from decimal import Decimal
import numpy as np
from utils import set_seed, convert_ext_examples, convert_cls_examples, logger
def do_convert():
set_seed(args.seed)
tic_time = time.time()
if not os.path.exists(args.doccano_file):
raise ValueError("Please input the correct path of doccano file.")
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
if len(args.splits) != 0 and len(args.splits) != 3:
raise ValueError("Only []/ len(splits)==3 accepted for splits.")
def _check_sum(splits):
return Decimal(str(splits[0])) + Decimal(str(splits[1])) + Decimal(
str(splits[2])) == Decimal("1")
if len(args.splits) == 3 and not _check_sum(args.splits):
raise ValueError(
"Please set correct splits, sum of elements in splits should be equal to 1."
)
with open(args.doccano_file, "r", encoding="utf-8") as f:
raw_examples = f.readlines()
def _create_ext_examples(examples,
negative_ratio,
prompt_prefix="情感倾向",
options=["正向", "负向"],
separator="##",
shuffle=False,
is_train=True):
entities, relations, aspects = convert_ext_examples(
examples, negative_ratio, prompt_prefix, options, separator,
is_train)
examples = entities + relations + aspects
if shuffle:
indexes = np.random.permutation(len(examples))
examples = [examples[i] for i in indexes]
return examples
def _create_cls_examples(examples, prompt_prefix, options, shuffle=False):
examples = convert_cls_examples(examples, prompt_prefix, options)
if shuffle:
indexes = np.random.permutation(len(examples))
examples = [examples[i] for i in indexes]
return examples
def _save_examples(save_dir, file_name, examples):
count = 0
save_path = os.path.join(save_dir, file_name)
if not examples:
logger.info("Skip saving %d examples to %s." % (0, save_path))
return
with open(save_path, "w", encoding="utf-8") as f:
for example in examples:
f.write(json.dumps(example, ensure_ascii=False) + "\n")
count += 1
logger.info("Save %d examples to %s." % (count, save_path))
if len(args.splits) == 0:
if args.task_type == "ext":
examples = _create_ext_examples(raw_examples, args.negative_ratio,
args.prompt_prefix, args.options,
args.separator, args.is_shuffle)
else:
examples = _create_cls_examples(raw_examples, args.prompt_prefix,
args.options, args.is_shuffle)
_save_examples(args.save_dir, "train.txt", examples)
else:
if args.is_shuffle:
indexes = np.random.permutation(len(raw_examples))
index_list = indexes.tolist()
raw_examples = [raw_examples[i] for i in indexes]
i1, i2, _ = args.splits
p1 = int(len(raw_examples) * i1)
p2 = int(len(raw_examples) * (i1 + i2))
train_ids = index_list[:p1]
dev_ids = index_list[p1:p2]
test_ids = index_list[p2:]
with open(os.path.join(args.save_dir, "sample_index.json"), "w") as fp:
maps = {
"train_ids": train_ids,
"dev_ids": dev_ids,
"test_ids": test_ids
}
fp.write(json.dumps(maps))
if args.task_type == "ext":
train_examples = _create_ext_examples(raw_examples[:p1],
args.negative_ratio,
args.prompt_prefix,
args.options, args.separator,
args.is_shuffle)
dev_examples = _create_ext_examples(raw_examples[p1:p2],
-1,
args.prompt_prefix,
args.options,
args.separator,
is_train=False)
test_examples = _create_ext_examples(raw_examples[p2:],
-1,
args.prompt_prefix,
args.options,
args.separator,
is_train=False)
else:
train_examples = _create_cls_examples(raw_examples[:p1],
args.prompt_prefix,
args.options)
dev_examples = _create_cls_examples(raw_examples[p1:p2],
args.prompt_prefix,
args.options)
test_examples = _create_cls_examples(raw_examples[p2:],
args.prompt_prefix,
args.options)
_save_examples(args.save_dir, "train.txt", train_examples)
_save_examples(args.save_dir, "dev.txt", dev_examples)
_save_examples(args.save_dir, "test.txt", test_examples)
logger.info('Finished! It takes %.2f seconds' % (time.time() - tic_time))
if __name__ == "__main__":
# yapf: disable
parser = argparse.ArgumentParser()
parser.add_argument("-d", "--doccano_file", default="./data/doccano.json",
type=str, help="The doccano file exported from doccano platform.")
parser.add_argument("-s", "--save_dir", default="./data",
type=str, help="The path of data that you wanna save.")
parser.add_argument("--negative_ratio", default=5, type=int,
help="Used only for the extraction task, the ratio of positive and negative samples, number of negtive samples = negative_ratio * number of positive samples")
parser.add_argument("--splits", default=[0.8, 0.1, 0.1], type=float, nargs="*",
help="The ratio of samples in datasets. [0.6, 0.2, 0.2] means 60%% samples used for training, 20%% for evaluation and 20%% for test.")
parser.add_argument("--task_type", choices=['ext', 'cls'], default="ext", type=str,
help="Select task type, ext for the extraction task and cls for the classification task, defaults to ext.")
parser.add_argument("--options", default=["正向", "负向"], type=str, nargs="+",
help="Used only for the classification task, the options for classification")
parser.add_argument("--prompt_prefix", default="情感倾向", type=str,
help="Used only for the classification task, the prompt prefix for classification")
parser.add_argument("--is_shuffle", default=True, type=bool,
help="Whether to shuffle the labeled dataset, defaults to True.")
parser.add_argument("--seed", type=int, default=1000,
help="Random seed for initialization")
parser.add_argument("--separator", type=str, default='##',
help="Used only for entity/aspect-level classification task, separator for entity label and classification label")
args = parser.parse_args()
# yapf: enable
do_convert()