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
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {Ember2018},
author=Christian Williams
},
year={2023}
}
"""
_DESCRIPTION = """\
This dataset is from the EMBER 2018 Malware Analysis dataset
"""
_HOMEPAGE = "https://github.com/elastic/ember"
_LICENSE = ""
_URLS = {
"text_classification": "https://huggingface.co/datasets/cw1521/ember2018-malware/blob/main/data/",
"test": "https://huggingface.co/datasets/cw1521/ember2018-malware/blob/main/data/*_train_1.jsonl"
}
class EMBERConfig(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="text_classification", version=VERSION, description="This part of my dataset covers text classification"),
datasets.BuilderConfig(name="test", version=VERSION, description="This part of my dataset is for testing")
]
DEFAULT_CONFIG_NAME = "text_classification"
def _info(self):
if self.config.name == "text_classification":
features = datasets.Features(
{
"input": datasets.Value("string"),
"label": datasets.Value("string"),
"x": datasets.features.Sequence(
datasets.Value("float32")
),
"y": datasets.Value("float32"),
"appeared": datasets.Value("string"),
"avclass": datasets.Value("string"),
"subset": datasets.Value("string"),
"sha256": datasets.Value("string")
}
)
else:
features = datasets.Features(
{
"input": datasets.Value("string"),
"label": datasets.Value("string"),
"x": datasets.features.Sequence(
datasets.Value("float32")
),
"y": datasets.Value("float32"),
"appeared": datasets.Value("string"),
"avclass": datasets.Value("string"),
"subset": datasets.Value("string"),
"sha256": datasets.Value("string")
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
# "*_train_*.jsonl"
# "*_test_*.jsonl"
def _split_generators(self, dl_manager):
urls = _URLS[self.config.name]
data_dir = dl_manager.download_and_extract(urls)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepaths": os.path.join(data_dir, "*_train_*.jsonl"),
"split": "train",
},
),
# datasets.SplitGenerator(
# name=datasets.Split.VALIDATION,
# gen_kwargs={
# "filepaths": os.path.join(data_dir, "*_valid_*.jsonl"),
# "split": "valid",
# },
# ),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepaths": os.path.join(data_dir, "*_test_*.jsonl"),
"split": "test"
},
)
]
def _generate_examples(self, filepaths, split):
key = 0
for id, filepath in enumerate(filepaths[split]):
with open(filepath[id], encoding="utf-8") as f:
data_list = json.load(f)
for data in data_list:
key += 1
if self.config.name == "text_classification":
yield key, {
"input": data["input"],
"label": data["label"],
"x": data["x"],
"y": data["y"],
"appeared": data["appeared"],
"avclass": data["avclass"],
"subset": data["subset"],
"sha256": data["sha256"]
}
else:
yield key, {
"input": data["input"],
"label": data["label"],
"x": data["x"],
"y": data["y"],
"appeared": data["appeared"],
"avclass": data["avclass"],
"subset": data["subset"],
"sha256": data["sha256"]
}
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