Datasets:
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"""Sydt Dataset"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ENCODING_DICS = {}
_BASE_FEATURE_NAMES = [
"salary",
"commission",
"age",
"education",
"car",
"zip",
"housevalue",
"yearsowned",
"loan",
"class",
]
DESCRIPTION = "Sydt dataset."
_HOMEPAGE = ""
_URLS = ("")
_CITATION = """"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/sydt/resolve/main/sydt.csv"
}
features_types_per_config = {
"sydt": {
"salary": datasets.Value("int64"),
"commission": datasets.Value("int64"),
"age": datasets.Value("int64"),
"education": datasets.Value("int64"),
"car": datasets.Value("int64"),
"zip": datasets.Value("string"),
"housevalue": datasets.Value("int64"),
"yearsowned": datasets.Value("int64"),
"loan": datasets.Value("int64"),
"class": datasets.ClassLabel(num_classes=2),
}
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class SydtConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(SydtConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Sydt(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "sydt"
BUILDER_CONFIGS = [SydtConfig(name="sydt", description="Sydt 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, header=None)
data = self.preprocess(data)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
data.columns = _BASE_FEATURE_NAMES
data = data[~data["class"].isna()]
data["class"] = data["class"].apply(lambda x: x - 1)
for feature in _ENCODING_DICS:
encoding_function = partial(self.encode, feature)
data[feature] = data[feature].apply(encoding_function)
return data[list(features_types_per_config[self.config.name].keys())]
def encode(self, feature, value):
if feature in _ENCODING_DICS:
return _ENCODING_DICS[feature][value]
raise ValueError(f"Unknown feature: {feature}")
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