spam_detect.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
from collections import defaultdict
import json
from pathlib import Path
import random
import re
from typing import Any, Dict, List, Tuple

import datasets


_urls = {
    "enron_spam": "data/enron_spam.jsonl",
    "enron_spam_subset": "data/enron_spam_subset.jsonl",
    "ling_spam": "data/ling_spam.jsonl",
    "sms_spam": "data/sms_spam.jsonl",
    "spam_assassin": "data/spam_assassin.jsonl",
    "spam_detection": "data/spam_detection.jsonl",
    "spam_emails": "data/spam_emails.jsonl",
    "spam_message": "data/spam_message.jsonl",
    "spam_message_lr": "data/spam_message_lr.jsonl",
    "trec07p": "data/trec07p.jsonl",
    "youtube_spam_collection": "data/youtube_spam_collection.jsonl",

}


_CITATION = """\
@dataset{spam_detect,
  author       = {Xing Tian},
  title        = {spam_detect},
  month        = sep,
  year         = 2023,
  publisher    = {Xing Tian},
  version      = {1.0},
}
"""


class SpamDetect(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")

    intent_configs = list()
    for name in _urls.keys():
        config = datasets.BuilderConfig(name=name, version=VERSION, description=name)
        intent_configs.append(config)

    BUILDER_CONFIGS = [
        *intent_configs,
    ]

    def _info(self):
        features = datasets.Features({
            "text": datasets.Value("string"),
            "label": datasets.Value("string"),
            "category": datasets.Value("string"),
            "data_source": datasets.Value("string"),
        })

        return datasets.DatasetInfo(
            features=features,
            supervised_keys=None,
            homepage="",
            license="",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        url = _urls[self.config.name]
        dl_path = dl_manager.download(url)
        archive_path = dl_path

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"archive_path": archive_path, "split": "train"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"archive_path": archive_path, "split": "validation"},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"archive_path": archive_path, "split": "test"},
            ),
        ]

    def _generate_examples(self, archive_path, split):
        """Yields examples."""
        archive_path = Path(archive_path)

        idx = 0

        with open(archive_path, "r", encoding="utf-8") as f:
            for row in f:
                sample = json.loads(row)

                if sample["split"] != split:
                    continue

                yield idx, {
                    "text": sample["text"],
                    "label": sample["label"],
                    "category": sample["category"],
                    "data_source": sample["data_source"],
                }
                idx += 1


if __name__ == '__main__':
    pass