examples/preprocess/process_youtube_spam_collection.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
import argparse
from collections import defaultdict
import json
import os
from pathlib import Path
import random
import re
import sys

pwd = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(pwd, '../../'))

import pandas as pd
from tqdm import tqdm

from project_settings import project_path


def get_args():
    parser = argparse.ArgumentParser()

    parser.add_argument("--data_dir", default="data/youtube_spam_collection", type=str)

    parser.add_argument(
        "--output_file",
        default=(project_path / "data/youtube_spam_collection.jsonl"),
        type=str
    )
    args = parser.parse_args()
    return args


def main():
    args = get_args()

    data_dir = Path(args.data_dir)

    with open(args.output_file, "w", encoding="utf-8") as f:
        for filename in data_dir.glob("*.csv"):
            df = pd.read_csv(filename.as_posix())

            for i, row in tqdm(df.iterrows(), total=len(df)):
                # print(row)
                text = row["CONTENT"]
                label = row["CLASS"]

                text = text.replace("", "")

                label = "spam" if label == 1 else "ham"

                if label not in ("spam", "ham"):
                    raise AssertionError

                num = random.random()
                if num < 0.9:
                    split = "train"
                elif num < 0.95:
                    split = "validation"
                else:
                    split = "test"

                row = {
                    "text": text,
                    "label": label,
                    "category": filename.stem,
                    "data_source": "youtube_spam_collection",
                    "split": split
                }
                row = json.dumps(row, ensure_ascii=False)
                f.write("{}\n".format(row))

    return


if __name__ == '__main__':
    main()