pol / pol.py
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from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "Pol dataset from the OpenML repository."
_HOMEPAGE = "https://www.openml.org/search?type=data&sort=runs&id=722&status=active"
_URLS = ("https://www.openml.org/search?type=data&sort=runs&id=722&status=active")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/pol/raw/main/pol.csv"
}
features_types_per_config = {
"pol": {
"f1": datasets.Value("int64"),
"f2": datasets.Value("int64"),
"f3": datasets.Value("int64"),
"f4": datasets.Value("int64"),
"f5": datasets.Value("int64"),
"f6": datasets.Value("int64"),
"f7": datasets.Value("int64"),
"f8": datasets.Value("int64"),
"f9": datasets.Value("int64"),
"f10": datasets.Value("int64"),
"f11": datasets.Value("int64"),
"f12": datasets.Value("int64"),
"f13": datasets.Value("int64"),
"f14": datasets.Value("int64"),
"f15": datasets.Value("int64"),
"f16": datasets.Value("int64"),
"f17": datasets.Value("int64"),
"f18": datasets.Value("int64"),
"f19": datasets.Value("int64"),
"f20": datasets.Value("int64"),
"f21": datasets.Value("int64"),
"f22": datasets.Value("int64"),
"f23": datasets.Value("int64"),
"f24": datasets.Value("int64"),
"f25": datasets.Value("int64"),
"f26": datasets.Value("int64"),
"f27": datasets.Value("int64"),
"f28": datasets.Value("int64"),
"f29": datasets.Value("int64"),
"f30": datasets.Value("int64"),
"f31": datasets.Value("int64"),
"f32": datasets.Value("int64"),
"f33": datasets.Value("int64"),
"f34": datasets.Value("int64"),
"f35": datasets.Value("int64"),
"f36": datasets.Value("int64"),
"f37": datasets.Value("int64"),
"f38": datasets.Value("int64"),
"f39": datasets.Value("int64"),
"f40": datasets.Value("int64"),
"f41": datasets.Value("int64"),
"f42": datasets.Value("int64"),
"f43": datasets.Value("int64"),
"f44": datasets.Value("int64"),
"f45": datasets.Value("int64"),
"f46": datasets.Value("int64"),
"f47": datasets.Value("int64"),
"f48": datasets.Value("int64"),
"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 ElectricityConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(ElectricityConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Electricity(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "pol"
BUILDER_CONFIGS = [
ElectricityConfig(name="pol",
description="Electricity 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