build_dataset.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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 json
import os

import datasets


def get_file_list():
    file_list = []
    with open("./file_list.json") as f:
        file_list = json.load(f)
    return file_list

# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Ember2018},
author={huggingface, Inc.
},
year={2023}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is from the EMBER 2018 dataset
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://github.com/elastic/ember"

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = ""

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
    "first_domain": "https://huggingface.co/datasets/cw1521/ember2018-malware/blob/main/data/"
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class NewDataset(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("1.1.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"),
        datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
    ]

    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
        if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "x": datasets.features.Sequence(
                            datasets.Value("float32")
                    ),
                    "y": datasets.Value("float32"),
                    "appeared": datasets.Value("string"),
                    "avclass": datasets.Value("string"),
                    "label": datasets.Value("string"),
                    "subset": datasets.Value("string"),
                    "sha256": datasets.Value("string")
                }
            )
        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
            features = datasets.Features(
                {
                    "x": datasets.features.Sequence(
                            datasets.Value("float32")
                    ),
                    "y": datasets.Value("float32"),
                    "appeared": datasets.Value("string"),
                    "avclass": datasets.Value("string"),
                    "label": datasets.Value("string"),
                    "subset": datasets.Value("string"),
                    "sha256": datasets.Value("string")
                }
            )
        return datasets.DatasetInfo(
            # 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=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=("sentence", "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 = _URLS[self.config.name]
        file_list = get_file_list()
        file_urls = {
            "train": [f"{urls[0]}/{file}" for file in file_list["train"]],
            "test": [f"{urls[0]}/{file}" for file in file_list["test"]]
        }
        data_dir = dl_manager.download_and_extract(file_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filenames": file_list["train"],
                    "local_datafiles": data_dir["train"],
                    "split": "train",
                },
            ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.VALIDATION,
            #     # These kwargs will be passed to _generate_examples
            #     gen_kwargs={
            #         "filepath": [os.path.join(data_dir, f"data/{file}") for file in file_list["dev"]],
            #         "split": "dev",
            #     },
            # ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filenames": file_list["train"],
                    "local_datafiles": data_dir["train"],
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filenames, local_datafiles):
        key = 0
        for id, path in enumerate(filenames):
            # TODO: 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.
            
            local_path = os.path.join(local_datafiles[id], path)
            with open(local_path, encoding="utf-8") as f:
                data_list = json.load(f)
            for data in data_list["data"]:
                key += 1
                if self.config.name == "first_domain":
                    # Yields examples as (key, example) tuples
                    yield key, {
                        "x": data["x"],
                        "y": data["y"],
                        "appeared": data["appeared"],
                        "avclass": data["avclass"],
                        "label": data["label"],
                        "subset": data["subset"],
                        "sha256": data["sha256"]
                    }
                else:
                    yield key, {
                        "x": data["x"],
                        "y": data["y"],
                        "appeared": data["appeared"],
                        "avclass": data["avclass"],
                        "label": data["label"],
                        "subset": data["subset"],
                        "sha256": data["sha256"]
                    }