|
from typing import List |
|
|
|
import datasets |
|
|
|
import pandas |
|
|
|
|
|
VERSION = datasets.Version("1.0.0") |
|
|
|
|
|
DESCRIPTION = "Madelon dataset from the UCI ML repository." |
|
_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/3/madelon" |
|
_URLS = ("https://archive-beta.ics.uci.edu/dataset/3/madelon") |
|
_CITATION = """""" |
|
|
|
|
|
urls_per_split = { |
|
"train": "https://huggingface.co/datasets/mstz/madelon/raw/main/madelon_train.csv", |
|
"validation": "https://huggingface.co/datasets/mstz/madelon/raw/main/madelon_valid.csv" |
|
} |
|
features_types_per_config = { |
|
"madelon": {str(i): datasets.Value("int16") for i in range(500)} |
|
} |
|
features_types_per_config["madelon"]["500"] = datasets.ClassLabel(num_classes=2) |
|
|
|
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
|
|
|
|
|
class MadelonConfig(datasets.BuilderConfig): |
|
def __init__(self, **kwargs): |
|
super(MadelonConfig, self).__init__(version=VERSION, **kwargs) |
|
self.features = features_per_config[kwargs["name"]] |
|
|
|
|
|
class Madelon(datasets.GeneratorBasedBuilder): |
|
|
|
DEFAULT_CONFIG = "madelon" |
|
BUILDER_CONFIGS = [ |
|
MadelonConfig(name="madelon", |
|
description="Madelon for multiclass classification.") |
|
] |
|
|
|
|
|
def _info(self): |
|
if self.config.name not in features_per_config: |
|
raise ValueError(f"Unknown configuration: {self.config.name}") |
|
|
|
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"]}), |
|
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloads["validation"]}) |
|
] |
|
|
|
def _generate_examples(self, filepath: str): |
|
data = pandas.read_csv(filepath) |
|
data = self.preprocess(data, config=self.config.name) |
|
|
|
for row_id, row in data.iterrows(): |
|
data_row = dict(row) |
|
|
|
yield row_id, data_row |
|
|
|
def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
|
data["500"] = data["500"].apply(lambda x: max(0, x)).astype(int) |
|
if "0.1" in data: |
|
data.drop("0.1", axis="columns", inplace=True) |
|
|
|
return data |
|
|
