Upload build_dataset.py

build_dataset.py

@@ -41,14 +41,16 @@ 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/"
+    "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 a first domain")
+        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" 
@@ -89,55 +91,45 @@ class EMBERConfig(datasets.GeneratorBasedBuilder):
             license=_LICENSE,
             citation=_CITATION,
         )
+    # "*_train_*.jsonl"
+    # "*_test_*.jsonl"
 
     def _split_generators(self, dl_manager):
         urls = _URLS[self.config.name]
-        file_list = get_file_list()
-        file_urls = {
-            "train": [f"{urls[0]}/{file}" for file in file_list["train"]],
-            "test": [f"{urls[0]}/{file}" for file in file_list["test"]]
-        }
-        data_dir = dl_manager.download_and_extract(file_urls)
+        data_dir = dl_manager.download_and_extract(urls)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "filenames": file_list["train"],
-                    "local_datafiles": data_dir["train"],
+                    "filepaths": os.path.join(data_dir, "*_train_*.jsonl"),
                     "split": "train",
                 },
             ),
             # datasets.SplitGenerator(
             #     name=datasets.Split.VALIDATION,
             #     gen_kwargs={
-            #         "filepath": [os.path.join(data_dir, f"data/{file}") for file in file_list["dev"]],
-            #         "split": "dev",
+            #         "filepaths": os.path.join(data_dir, "*_valid_*.jsonl"),
+            #         "split": "valid",
             #     },
             # ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "filenames": file_list["test"],
-                    "local_datafiles": data_dir["test"],
+                    "filepaths": os.path.join(data_dir, "*_test_*.jsonl"),
                     "split": "test"
                 },
-            ),
+            )
         ]
 
 
-    def _generate_examples(self, filenames, local_datafiles):
+    def _generate_examples(self, filepaths, split):
         key = 0
-        for id, path in enumerate(filenames):
-            # 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.
-            
-            local_path = os.path.join(local_datafiles[id], path)
-            with open(local_path, encoding="utf-8") as f:
+        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["data"]:
                 key += 1
                 if self.config.name == "text_classification":
-                    # Yields examples as (key, example) tuples
                     yield key, {
                         "x": data["x"],
                         "y": data["y"],