preprocess.py

# 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
import numpy as np

from utils import set_seed, convert_ext_examples


def do_convert():
    set_seed(args.seed)

    tic_time = time.time()
    if not os.path.exists(args.input_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.")

    if args.splits and sum(args.splits) != 1:
        raise ValueError(
            "Please set correct splits, sum of elements in splits should be equal to 1."
        )

    with open(args.input_file, "r", encoding="utf-8") as f:
        raw_examples = f.readlines()

    def _create_ext_examples(examples, negative_ratio=0, shuffle=False):
        entities, relations = convert_ext_examples(examples, negative_ratio)
        examples = [e + r for e, r in zip(entities, relations)]
        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)
        with open(save_path, "w", encoding="utf-8") as f:
            for example in examples:
                for x in example:
                    f.write(json.dumps(x, ensure_ascii=False) + "\n")
                    count += 1
        print("\nSave %d examples to %s." % (count, save_path))

    if len(args.splits) == 0:
        examples = _create_ext_examples(raw_examples, args.negative_ratio,
                                        args.is_shuffle)
        _save_examples(args.save_dir, "train.txt", examples)
    else:
        if args.is_shuffle:
            indexes = np.random.permutation(len(raw_examples))
            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_examples = _create_ext_examples(raw_examples[:p1],
                                              args.negative_ratio,
                                              args.is_shuffle)
        dev_examples = _create_ext_examples(raw_examples[p1:p2])
        test_examples = _create_ext_examples(raw_examples[p2:])

        _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)

    print('Finished! It takes %.2f seconds' % (time.time() - tic_time))


if __name__ == "__main__":
    # yapf: disable
    parser = argparse.ArgumentParser()

    parser.add_argument("--input_file", default="./data/data.json", type=str, help="The data file exported from doccano platform.")
    parser.add_argument("--save_dir", default="./data", type=str, help="The path to save processed data.")
    parser.add_argument("--negative_ratio", default=5, type=int, help="Used only for the classification 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("--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")

    args = parser.parse_args()
    # yapf: enable

    do_convert()