difraud.py

import csv
import json
import os
import sys
import datasets
from datasets.tasks import TextClassification


# TODO: Add BibTeX citation
_CITATION = """
TODO: Add citation here
"""

_DESCRIPTION = """
DIFrauD -- (Domain Independent Fraud Detection) is a corpus of deceptive and truthful texts from 7 domains:

"fake_news",
"job_scams",
"phishing",
"political_statements",
"product_reviews",
"sms",
"twitter_rumours"

To load a specific domain, pass it as the "name" parameter to load_dataset()
"""

_HOMEPAGE = "http://cs.uh.edu/~rmverma/ra2.html"

_LICENSE = """
    Copyright 2023 University of Houston
    
    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
    (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge,
    publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
    subject to the following conditions:
    
    The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
    
    THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
    OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
    LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR 
    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
    """

class DIFrauD(datasets.GeneratorBasedBuilder):
    """Domain Independent Fraud Detection benchmarks -- a Large multi-domain english corpus of truthful and deceptive texts"""

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="fake_news", description="Fake News domain"),
        datasets.BuilderConfig(name="job_scams", description="Online Job Scams"),
        datasets.BuilderConfig(name="phishing", description="Email phishing attacks"),
        datasets.BuilderConfig(name="political_statements", description="Statements by various politicians"),
        datasets.BuilderConfig(name="product_reviews", description="Amazon product reviews"),
        datasets.BuilderConfig(name="sms", description="SMS spam and phishing attacks"),
        datasets.BuilderConfig(name="twitter_rumours", 
                               description="Collection of rumours from twitter spanning several years and topics"),
    ]

    DEFAULT_CONFIG_NAME = "phishing"

    def _info(self):
        self.features = datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "label": datasets.ClassLabel(num_classes=2, names=['non-deceptive', 'deceptive']),
                }
        )
        return datasets.DatasetInfo(
            config_name=self.config.name,
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=self.features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            supervised_keys=("text", "label"),
            # specify standard binary classification task for datasets to setup easier
            task_templates=[TextClassification(text_column="text", label_column="label")],
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        urls = {
            "train": self.config.name+"/train.jsonl",
            "test": self.config.name+"/test.jsonl",
            "validation": self.config.name+"/validation.jsonl",
        }
        data_dir = dl_manager.download_and_extract(urls)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir['train']),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": os.path.join(data_dir['validation']),
                    "split": "validation",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": os.path.join(data_dir['test']),
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                yield key, {
                    "text": data["text"],
                    "label": int(data["label"]),
                }