Datasets:
Tasks:
Tabular Classification
Languages:
English
| """Dexter Dataset""" | |
| from typing import List | |
| from functools import partial | |
| import datasets | |
| import pandas | |
| VERSION = datasets.Version("1.0.0") | |
| _ENCODING_DICS = { | |
| } | |
| DESCRIPTION = "Dexter dataset." | |
| _HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/168/dexter" | |
| _URLS = ("https://archive-beta.ics.uci.edu/dataset/168/dexter") | |
| _CITATION = """ | |
| @misc{misc_dexter_168, | |
| author = {Guyon,Isabelle, Gunn,Steve, Ben-Hur,Asa & Dror,Gideon}, | |
| title = {{Dexter}}, | |
| year = {2008}, | |
| howpublished = {UCI Machine Learning Repository}, | |
| note = {{DOI}: \\url{10.24432/C5P898}} | |
| } | |
| """ | |
| # Dataset info | |
| urls_per_split = { | |
| "train": "https://huggingface.co/datasets/mstz/dexter/resolve/main/dexter.data" | |
| } | |
| features_types_per_config = { | |
| "dexter": {f"feature_{i}": datasets.Value("float64") for i in range(20000)} | |
| } | |
| features_types_per_config["dexter"]["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 DexterConfig(datasets.BuilderConfig): | |
| def __init__(self, **kwargs): | |
| super(DexterConfig, self).__init__(version=VERSION, **kwargs) | |
| self.features = features_per_config[kwargs["name"]] | |
| class Dexter(datasets.GeneratorBasedBuilder): | |
| # dataset versions | |
| DEFAULT_CONFIG = "dexter" | |
| BUILDER_CONFIGS = [DexterConfig(name="dexter", description="Dexter 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) | |
| 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 = [f"feature_{i}" for i in range(20000)] + ["class"] | |
| print(data.columns) | |
| for feature in _ENCODING_DICS: | |
| encoding_function = partial(self.encode, feature) | |
| data.loc[:, 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}") | |