Upload build_dataset.py

build_dataset.py

@@ -25,63 +25,36 @@ def get_file_list():
         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.
+author=Christian Williams
 },
 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
+This dataset is from the EMBER 2018 Malware Analysis 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/"
+    "text_classification": "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."""
-
+class EMBERConfig(datasets.GeneratorBasedBuilder):
     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"),
+        datasets.BuilderConfig(name="text_classification", version=VERSION, description="This part of my dataset covers a first domain")
     ]
 
-    DEFAULT_CONFIG_NAME = "first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+    DEFAULT_CONFIG_NAME = "text_classification" 
 
     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
+        if self.config.name == "text_classification": 
             features = datasets.Features(
                 {
                     "x": datasets.features.Sequence(
@@ -95,7 +68,7 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                     "sha256": datasets.Value("string")
                 }
             )
-        else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
+        else: 
             features = datasets.Features(
                 {
                     "x": datasets.features.Sequence(
@@ -110,28 +83,14 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                 }
             )
         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
+            features=features, 
             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 = {
@@ -142,7 +101,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         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"],
@@ -151,7 +109,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             ),
             # 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",
@@ -159,16 +116,15 @@ class NewDataset(datasets.GeneratorBasedBuilder):
             # ),
             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"],
+                    "filenames": file_list["test"],
+                    "local_datafiles": data_dir["test"],
                     "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):
@@ -180,7 +136,7 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                 data_list = json.load(f)
             for data in data_list["data"]:
                 key += 1
-                if self.config.name == "first_domain":
+                if self.config.name == "text_classification":
                     # Yields examples as (key, example) tuples
                     yield key, {
                         "x": data["x"],