Datasets:
Upload 2 files
Browse files- build_dataset.py +15 -9
- file_list.json +5 -1
build_dataset.py
CHANGED
|
@@ -51,7 +51,7 @@ _LICENSE = ""
|
|
| 51 |
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
|
| 52 |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
|
| 53 |
_URLS = {
|
| 54 |
-
"first_domain": "https://huggingface.co/datasets/cw1521/ember2018-malware/blob/main/data
|
| 55 |
}
|
| 56 |
|
| 57 |
|
|
@@ -133,15 +133,19 @@ class NewDataset(datasets.GeneratorBasedBuilder):
|
|
| 133 |
# 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.
|
| 134 |
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
|
| 135 |
urls = _URLS[self.config.name]
|
| 136 |
-
data_dir = dl_manager.download_and_extract(urls)
|
| 137 |
-
print(data_dir)
|
| 138 |
file_list = get_file_list()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 139 |
return [
|
| 140 |
datasets.SplitGenerator(
|
| 141 |
name=datasets.Split.TRAIN,
|
| 142 |
# These kwargs will be passed to _generate_examples
|
| 143 |
gen_kwargs={
|
| 144 |
-
"filepaths":
|
|
|
|
| 145 |
"split": "train",
|
| 146 |
},
|
| 147 |
),
|
|
@@ -156,21 +160,23 @@ class NewDataset(datasets.GeneratorBasedBuilder):
|
|
| 156 |
datasets.SplitGenerator(
|
| 157 |
name=datasets.Split.TEST,
|
| 158 |
# These kwargs will be passed to _generate_examples
|
| 159 |
-
# [os.path.join(data_dir, file) for file in file_list["test"]],
|
| 160 |
gen_kwargs={
|
| 161 |
-
"filepaths":
|
|
|
|
| 162 |
"split": "test"
|
| 163 |
},
|
| 164 |
),
|
| 165 |
]
|
| 166 |
|
| 167 |
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
| 168 |
-
def _generate_examples(self,
|
| 169 |
key = 0
|
| 170 |
-
for path in
|
| 171 |
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
|
| 172 |
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
|
| 173 |
-
|
|
|
|
|
|
|
| 174 |
data_list = json.load(f)
|
| 175 |
for data in data_list["data"]:
|
| 176 |
key += 1
|
|
|
|
| 51 |
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
|
| 52 |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
|
| 53 |
_URLS = {
|
| 54 |
+
"first_domain": "https://huggingface.co/datasets/cw1521/ember2018-malware/blob/main/data/"
|
| 55 |
}
|
| 56 |
|
| 57 |
|
|
|
|
| 133 |
# 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.
|
| 134 |
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
|
| 135 |
urls = _URLS[self.config.name]
|
|
|
|
|
|
|
| 136 |
file_list = get_file_list()
|
| 137 |
+
file_urls = {
|
| 138 |
+
"train": [f"{urls[0]}/{file}" for file in file_list["train"]],
|
| 139 |
+
"test": [f"{urls[0]}/{file}" for file in file_list["test"]]
|
| 140 |
+
}
|
| 141 |
+
data_dir = dl_manager.download_and_extract(file_urls)
|
| 142 |
return [
|
| 143 |
datasets.SplitGenerator(
|
| 144 |
name=datasets.Split.TRAIN,
|
| 145 |
# These kwargs will be passed to _generate_examples
|
| 146 |
gen_kwargs={
|
| 147 |
+
"filepaths": file_list["train"],
|
| 148 |
+
"local_datafiles": data_dir["train"],
|
| 149 |
"split": "train",
|
| 150 |
},
|
| 151 |
),
|
|
|
|
| 160 |
datasets.SplitGenerator(
|
| 161 |
name=datasets.Split.TEST,
|
| 162 |
# These kwargs will be passed to _generate_examples
|
|
|
|
| 163 |
gen_kwargs={
|
| 164 |
+
"filepaths": file_list["train"],
|
| 165 |
+
"local_datafiles": data_dir["train"],
|
| 166 |
"split": "test"
|
| 167 |
},
|
| 168 |
),
|
| 169 |
]
|
| 170 |
|
| 171 |
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
|
| 172 |
+
def _generate_examples(self, filenames, local_datafiles):
|
| 173 |
key = 0
|
| 174 |
+
for id, path in enumerate(filenames):
|
| 175 |
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
|
| 176 |
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
|
| 177 |
+
|
| 178 |
+
local_path = os.path.join(local_datafiles[id], path)
|
| 179 |
+
with open(local_path, encoding="utf-8") as f:
|
| 180 |
data_list = json.load(f)
|
| 181 |
for data in data_list["data"]:
|
| 182 |
key += 1
|
file_list.json
CHANGED
|
@@ -1 +1,5 @@
|
|
| 1 |
-
{
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"train": ["ember2018_train_1.jsonl"],
|
| 3 |
+
|
| 4 |
+
"test": ["ember2018_test_1.jsonl"]
|
| 5 |
+
}
|