fixed data loader

shellcode_ia32.py

@@ -54,7 +54,7 @@ _URLs = {
 
 
 # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class Shellcode_IA32(datasets.GeneratorBasedBuilder):
+class ShellcodeIA32(datasets.GeneratorBasedBuilder):
     """Shellcode_IA32 a dataset for shellcode generation"""
 
     VERSION = datasets.Version("1.1.0")
@@ -87,22 +87,22 @@ class Shellcode_IA32(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,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
+            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,
+                # specify them here. They'll be used if as_supervised=True in
+                # builder.as_dataset.
+                supervised_keys=None,
+                # 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):
         """Returns SplitGenerators."""
@@ -114,12 +114,39 @@ class Shellcode_IA32(datasets.GeneratorBasedBuilder):
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
         my_urls = _URLs[self.config.name]
         data_dir = dl_manager.download_and_extract(my_urls)
+        # return [
+        #     datasets.SplitGenerator(
+        #         name=datasets.Split.TRAIN,
+        #         # These kwargs will be passed to _generate_examples
+        #         gen_kwargs={
+        #             "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
+        #             "split": "train",
+        #         },
+        #     ),
+        #     datasets.SplitGenerator(
+        #         name=datasets.Split.TEST,
+        #         # These kwargs will be passed to _generate_examples
+        #         gen_kwargs={
+        #             "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
+        #             "split": "test"
+        #         },
+        #     ),
+        #     datasets.SplitGenerator(
+        #         name=datasets.Split.VALIDATION,
+        #         # These kwargs will be passed to _generate_examples
+        #         gen_kwargs={
+        #             "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
+        #             "split": "dev",
+        #         },
+        #     ),
+        # ]
+
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
+                    "filepath": os.path.join(data_dir),
                     "split": "train",
                 },
             ),
@@ -127,7 +154,7 @@ class Shellcode_IA32(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.TEST,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
+                    "filepath": os.path.join(data_dir),
                     "split": "test"
                 },
             ),
@@ -135,7 +162,7 @@ class Shellcode_IA32(datasets.GeneratorBasedBuilder):
                 name=datasets.Split.VALIDATION,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "filepath": os.path.join(data_dir, "Shellcode_IA32.tsv"),
+                    "filepath": os.path.join(data_dir),
                     "split": "dev",
                 },
             ),
@@ -147,20 +174,20 @@ class Shellcode_IA32(datasets.GeneratorBasedBuilder):
         """ Yields examples as (key, example) tuples. """
         # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is here for legacy reason (tfds) and is not important in itself.
-            """This function returns the examples in the raw (text) form."""
-
-            df = pd.read_csv(filepath, delimiter = '\t')
-            train = df.sample(frac = 0.8, random_state = 0)
-            test = df.drop(train.index)
-            dev = test.sample(frac = 0.5, random_state = 0)
-            test = test.drop(dev.index)
-
-            if split == 'train':
-                data = train
-            elif split == 'dev':
-                data = dev
-            elif split == 'test':
-                data = test
+        """This function returns the examples in the raw (text) form."""
+
+        df = pd.read_csv(filepath, delimiter = '\t')
+        train = df.sample(frac = 0.8, random_state = 0)
+        test = df.drop(train.index)
+        dev = test.sample(frac = 0.5, random_state = 0)
+        test = test.drop(dev.index)
+
+        if split == 'train':
+            data = train
+        elif split == 'dev':
+            data = dev
+        elif split == 'test':
+            data = test
 
         for idx, row in data.iterrows():
             yield idx, {