Datasets:
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# 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.zip"
}
# 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]
data_dir = dl_manager.download_and_extract(urls)
print(data_dir)
file_list = get_file_list()
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepaths": [os.path.join(data_dir, f"data/{file}") for file in file_list["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
# [os.path.join(data_dir, file) for file in file_list["test"]],
gen_kwargs={
"filepaths": [os.path.join(data_dir, f"data/{file}") for file in file_list["test"]],
"split": "test"
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepaths, split):
key = 0
for path in filepaths:
# 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.
with open(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"]
}
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