Datasets:
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from typing import List
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
DESCRIPTION = "House16 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/house16/raw/main/house_16H.csv"
}
features_types_per_config = {
"house16": {
"P1": datasets.Value("int64"),
"P5p1": datasets.Value("float64"),
"P6p2": datasets.Value("float64"),
"P11p4": datasets.Value("float64"),
"P14p9": datasets.Value("float64"),
"P15p1": datasets.Value("float64"),
"P15p3": datasets.Value("float64"),
"P16p2": datasets.Value("float64"),
"P18p2": datasets.Value("float64"),
"P27p4": datasets.Value("float64"),
"H2p2": datasets.Value("float64"),
"H8p2": datasets.Value("float64"),
"H10p1": datasets.Value("float64"),
"H13p1": datasets.Value("float64"),
"H18pA": datasets.Value("float64"),
"H40p4": 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 House16Config(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(House16Config, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class House16(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "house16"
BUILDER_CONFIGS = [
House16Config(name="house16",
description="House16 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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