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import datasets
import pandas as pd
import os
class MyDataset(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(
description="DESCRIPTION",
features=datasets.Features(
{"domain": datasets.Value("string"), "label": datasets.Value("string")}
),
supervised_keys=("domain", "label"),
homepage="_HOMEPAGE",
)
def _split_generators(self, dl_manager: datasets.DownloadConfig):
# Load your local dataset file
csv_path = "https://huggingface.co/datasets/harpomaxx/dga-detection/raw/main/argencon.csv.gz"
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"filepath": csv_path,
"split": split,
},
)
for split in ["train", "test", "validation"]
]
def _generate_examples_old(
self,
filepath: str,
split: str,
):
# Read your CSV dataset
dataset = pd.read_csv(filepath)
# 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"],
}
def _generate_examples(
self,
filepath: str,
split: str,
):
# Read your CSV dataset
dataset = pd.read_csv(filepath)
# Get the total number of rows
total_rows = len(dataset)
# Define the ratio for train, test, and validation splits
train_ratio = 0.7
test_ratio = 0.2
# Calculate the indices for each split
train_end = int(train_ratio * total_rows)
test_end = train_end + int(test_ratio * total_rows)
# Filter your dataset based on the 'split' argument
if split == "train":
dataset = dataset.iloc[:train_end]
elif split == "test":
dataset = dataset.iloc[train_end:test_end]
elif split == "validation":
dataset = dataset.iloc[test_end:]
# Generate examples
for index, row in dataset.iterrows():
yield index, {
"domain": row["domain"],
"label": row["label"],
}
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