Delete CIC-IDS2017.py

CIC-IDS2017.py

@@ -1,62 +0,0 @@
-"""CIC-IDS2017"""
-
-import os
-import pyarrow.parquet as pq
-import datasets
-
-_DESCRIPTION = ""
-
-_HOMEPAGE = ""
-
-_URLS = {
-    "Network-Flows": "Network-Flows/CICIDS_Flow.parquet",
-    "Packet-Bytes": "Packet-Bytes/Packet_Bytes_File_1.parquet",
-    "Packet-Fields": "Packet-Fields/Packet_Fields_File_1.parquet",
-    "Payload-Bytes": "Payload-Bytes/Payload_Bytes_File_1.parquet",
-}
-
-class NewDataset(datasets.GeneratorBasedBuilder):
-    VERSION = datasets.Version("1.0.0")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="Network-Flows", version=VERSION, description="Network-Flows"),
-        datasets.BuilderConfig(name="Packet-Bytes", version=VERSION, description="Packet-Bytes"),
-        datasets.BuilderConfig(name="Packet-Fields", version=VERSION, description="Packet-Fields"),
-        datasets.BuilderConfig(name="Payload-Bytes", version=VERSION, description="Payload-Bytes"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "Network-Flows"
-
-    def _info(self):
-        # infer features directly from Parquet file schema
-        parquet_file = pq.ParquetFile(_URLS[self.config.name])
-        schema = parquet_file.schema
-        # Only take the first 1000 fields
-        limited_schema = pa.schema(schema[:1000])
-        features = datasets.Features.from_arrow_schema(limited_schema)
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=features,
-            homepage=_HOMEPAGE,
-        )
-
-    def _split_generators(self, dl_manager):
-        url = _URLS[self.config.name]
-        downloaded_file = dl_manager.download_and_extract(url)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": downloaded_file,
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        parquet_file = pq.ParquetFile(filepath)
-        for row_index in range(parquet_file.num_row_groups):
-            row_group = parquet_file.read_row_group(row_index)
-            # Convert to pandas DataFrame, select first 1000 columns and convert to dictionary
-            df = row_group.to_pandas()
-            selected_columns = df.iloc[:, :1000]
-            yield row_index, selected_columns.to_dict('records')[0]