Datasets:

cw1521
/

ember2018-malware

+# 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": "./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)
+        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"]
+                    }

data.zip ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:7bd4647626eaa106715150ccf876ed428fd311a7ac9f9bdd19c22da1bf2b9170
+size 4855764817

file_list.json ADDED Viewed

	@@ -0,0 +1 @@

+ {"train": ["ember2018_train_1.jsonl", "ember2018_train_2.jsonl", "ember2018_train_3.jsonl", "ember2018_train_4.jsonl", "ember2018_train_5.jsonl", "ember2018_train_6.jsonl", "ember2018_train_7.jsonl", "ember2018_train_8.jsonl", "ember2018_train_9.jsonl", "ember2018_train_10.jsonl", "ember2018_train_11.jsonl", "ember2018_train_12.jsonl", "ember2018_train_13.jsonl", "ember2018_train_14.jsonl", "ember2018_train_15.jsonl", "ember2018_train_16.jsonl", "ember2018_train_17.jsonl", "ember2018_train_18.jsonl", "ember2018_train_19.jsonl", "ember2018_train_20.jsonl", "ember2018_train_21.jsonl", "ember2018_train_22.jsonl", "ember2018_train_23.jsonl", "ember2018_train_24.jsonl", "ember2018_train_25.jsonl", "ember2018_train_26.jsonl", "ember2018_train_27.jsonl", "ember2018_train_28.jsonl", "ember2018_train_29.jsonl", "ember2018_train_30.jsonl", "ember2018_train_31.jsonl", "ember2018_train_32.jsonl", "ember2018_train_33.jsonl", "ember2018_train_34.jsonl", "ember2018_train_35.jsonl", "ember2018_train_36.jsonl", "ember2018_train_37.jsonl", "ember2018_train_38.jsonl", "ember2018_train_39.jsonl", "ember2018_train_40.jsonl", "ember2018_train_41.jsonl", "ember2018_train_42.jsonl", "ember2018_train_43.jsonl", "ember2018_train_44.jsonl", "ember2018_train_45.jsonl", "ember2018_train_46.jsonl", "ember2018_train_47.jsonl", "ember2018_train_48.jsonl", "ember2018_train_49.jsonl", "ember2018_train_50.jsonl", "ember2018_train_51.jsonl", "ember2018_train_52.jsonl", "ember2018_train_53.jsonl", "ember2018_train_54.jsonl", "ember2018_train_55.jsonl", "ember2018_train_56.jsonl", "ember2018_train_57.jsonl", "ember2018_train_58.jsonl", "ember2018_train_59.jsonl", "ember2018_train_60.jsonl", "ember2018_train_61.jsonl", "ember2018_train_62.jsonl", "ember2018_train_63.jsonl", "ember2018_train_64.jsonl", "ember2018_train_65.jsonl", "ember2018_train_66.jsonl", "ember2018_train_67.jsonl", "ember2018_train_68.jsonl", "ember2018_train_69.jsonl", "ember2018_train_70.jsonl", "ember2018_train_71.jsonl", "ember2018_train_72.jsonl", "ember2018_train_73.jsonl", "ember2018_train_74.jsonl", "ember2018_train_75.jsonl", "ember2018_train_76.jsonl", "ember2018_train_77.jsonl", "ember2018_train_78.jsonl", "ember2018_train_79.jsonl", "ember2018_train_80.jsonl"], "test": ["ember2018_test_1.jsonl", "ember2018_test_2.jsonl", "ember2018_test_3.jsonl", "ember2018_test_4.jsonl", "ember2018_test_5.jsonl", "ember2018_test_6.jsonl", "ember2018_test_7.jsonl", "ember2018_test_8.jsonl", "ember2018_test_9.jsonl", "ember2018_test_10.jsonl", "ember2018_test_11.jsonl", "ember2018_test_12.jsonl", "ember2018_test_13.jsonl", "ember2018_test_14.jsonl", "ember2018_test_15.jsonl", "ember2018_test_16.jsonl", "ember2018_test_17.jsonl", "ember2018_test_18.jsonl", "ember2018_test_19.jsonl", "ember2018_test_20.jsonl"]}