Update dga-detection.py

dga-detection.py

@@ -10,12 +10,11 @@ of DGA domains of Andrey Abakumov and John Bambenek. The total amount of DGA dom
 were generated by 51 different malware families. About the 55% of of the DGA portion of dataset is composed of samples from the Banjori, Post, Timba, Cryptolocker, 
 Ramdo and Conficker malware.
 """
-
 _HOMEPAGE = "https://https://huggingface.co/datasets/harpomaxx/dga-detection"
 
 class MyDataset(datasets.GeneratorBasedBuilder):
     def _info(self):
-
+        # Provide metadata for the dataset
         return datasets.DatasetInfo(
             description=_DESCRIPTION,
             features=datasets.Features(
@@ -24,15 +23,15 @@ class MyDataset(datasets.GeneratorBasedBuilder):
                  "class": datasets.Value("int")
                 }
             ),
-            supervised_keys=("domain", "label"),
+            supervised_keys=("domain", "class"),
             homepage="_HOMEPAGE",
         )
 
-
     def _split_generators(self, dl_manager: datasets.DownloadConfig):
-        # Load your local dataset file
+        # Load your dataset file
         csv_path = "https://huggingface.co/datasets/harpomaxx/dga-detection/resolve/main/argencon.csv.gz"
 
+        # Create SplitGenerators for each dataset split (train, test, validation)
         return [
             datasets.SplitGenerator(
                 name=split,
@@ -44,24 +43,14 @@ class MyDataset(datasets.GeneratorBasedBuilder):
             for split in ["train", "test", "validation"]
         ]
 
-    def _generate_examples_old(
-        self,
-        filepath: str,
-        split: str,
-    ):
-        # Read your CSV dataset
-        dataset = pd.read_csv(filepath,compression='gzip')
-
-        # You can filter or split your dataset based on the 'split' argument if necessary
-        dataset = dataset[dataset["split"] == split]
-        # Generate examples
-        for index, row in dataset.iterrows():
-            yield index, {
-                "domain": row["domain"],
-                "label": row["label"],
-            }
-
-    
+    """""
+    The data variable in the _generate_examples() method is a temporary variable that holds the portion of the dataset based on the current split. 
+    The datasets.SplitGenerator in the _split_generators() method is responsible for creating the three different keys ('train', 'test', 'validation').When you load your 
+    dataset using load_dataset(), the Hugging Face Datasets library will automatically call the _split_generators() method to create the three different dataset splits. 
+    Then, it will call the _generate_examples() method for each split separately, passing the corresponding split name as the split argument. 
+    This is how the different keys are created. To clarify, the _generate_examples() method processes one split at a time, and the Datasets library combines the results 
+    to create a final dataset with keys for 'train', 'test', and 'validation'.
+    """
     def _generate_examples(
         self,
         filepath: str,
@@ -69,7 +58,10 @@ class MyDataset(datasets.GeneratorBasedBuilder):
     ):
         # Read your CSV dataset
         dataset = pd.read_csv(filepath,compression='gzip')
+
+        # Create the 'class' column based on the 'label' column
         dataset['class'] = dataset['label'].apply(lambda x: 0 if 'normal' in x else 1)
+
         # Get the total number of rows
         total_rows = len(dataset)
 
@@ -96,4 +88,3 @@ class MyDataset(datasets.GeneratorBasedBuilder):
                 "label": row["label"],
                 "class": row["class"],
             }
-