Datasets:
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"""TwoNorm"""
from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "TwoNorm dataset from the OpenML repository."
_HOMEPAGE = "https://www.openml.org/search?type=data&status=active&id=1507"
_URLS = ("https://www.openml.org/search?type=data&status=active&id=1507")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/twonorm/raw/main/twonorm.csv"
}
features_types_per_config = {
"twonorm": {
"V1": datasets.Value("float64"),
"V2": datasets.Value("float64"),
"V3": datasets.Value("float64"),
"V4": datasets.Value("float64"),
"V5": datasets.Value("float64"),
"V6": datasets.Value("float64"),
"V7": datasets.Value("float64"),
"V8": datasets.Value("float64"),
"V9": datasets.Value("float64"),
"V10": datasets.Value("float64"),
"V11": datasets.Value("float64"),
"V12": datasets.Value("float64"),
"V13": datasets.Value("float64"),
"V14": datasets.Value("float64"),
"V15": datasets.Value("float64"),
"V16": datasets.Value("float64"),
"V17": datasets.Value("float64"),
"V18": datasets.Value("float64"),
"V19": datasets.Value("float64"),
"V20": datasets.Value("float64"),
"class": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class TwoNormConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(TwoNormConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class TwoNorm(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "twonorm"
BUILDER_CONFIGS = [
TwoNormConfig(name="twonorm",
description="TwoNorm for binary classification.")
]
def _info(self):
info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
features=features_per_config[self.config.name])
return info
def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
downloads = dl_manager.download_and_extract(urls_per_split)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]})
]
def _generate_examples(self, filepath: str):
data = pandas.read_csv(filepath)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
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