| """Arcene Dataset""" |
|
|
| from typing import List |
| from functools import partial |
|
|
| import datasets |
|
|
| import pandas |
|
|
|
|
| VERSION = datasets.Version("1.0.0") |
|
|
| _ENCODING_DICS = { |
| } |
|
|
| DESCRIPTION = "Arcene dataset." |
| _HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/167/arcene" |
| _URLS = ("https://archive-beta.ics.uci.edu/dataset/167/arcene") |
| _CITATION = """ |
| @misc{misc_arcene_167, |
| author = {Guyon,Isabelle, Gunn,Steve, Ben-Hur,Asa & Dror,Gideon}, |
| title = {{Arcene}}, |
| year = {2008}, |
| howpublished = {UCI Machine Learning Repository}, |
| note = {{DOI}: \\url{10.24432/C58P55}} |
| } |
| """ |
|
|
| |
| urls_per_split = { |
| "train": "https://huggingface.co/datasets/mstz/arcene/raw/main/arcene_train.data" |
| } |
| features_types_per_config = { |
| "arcene": {f"feature_{i}": datasets.Value("int64") for i in range(10000)} |
| } |
| features_types_per_config["arcene"]["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 ArceneConfig(datasets.BuilderConfig): |
| def __init__(self, **kwargs): |
| super(ArceneConfig, self).__init__(version=VERSION, **kwargs) |
| self.features = features_per_config[kwargs["name"]] |
|
|
|
|
| class Arcene(datasets.GeneratorBasedBuilder): |
| |
| DEFAULT_CONFIG = "arcene" |
| BUILDER_CONFIGS = [ArceneConfig(name="arcene", description="Arcene 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) |
| columns = [f"feature_{i}" for i in range(10000)] + ["class"] |
| data.columns = columns |
|
|
| for row_id, row in data.iterrows(): |
| print(f"on {row_id}") |
| data_row = dict(row) |
|
|
| yield row_id, data_row |
|
|
| def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame: |
| 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}") |
|
|
