| from typing import List |
|
|
| import datasets |
|
|
| import pandas |
|
|
|
|
| VERSION = datasets.Version("1.0.0") |
| _BASE_FEATURE_NAMES = [ |
| "color", |
| "size", |
| "act", |
| "age", |
| "is_inflated" |
| ] |
|
|
|
|
| DESCRIPTION = "Balloons dataset from the UCI ML repository." |
| _HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Balloons" |
| _URLS = ("https://huggingface.co/datasets/mstz/balloons/raw/balloons.csv") |
| _CITATION = """ |
| @misc{misc_balloons_13, |
| title = {{Balloons}}, |
| howpublished = {UCI Machine Learning Repository}, |
| note = {{DOI}: \\url{10.24432/C5BP4D}} |
| }""" |
|
|
| |
| urls_per_split = { |
| "adult_or_stretch": {"train": "https://huggingface.co/datasets/mstz/balloons/raw/main/adult+stretch.data"}, |
| "adult_and_stretch": {"train": "https://huggingface.co/datasets/mstz/balloons/raw/main/adult-stretch.data"}, |
| "yellow_and_small": {"train": "https://huggingface.co/datasets/mstz/balloons/raw/main/yellow-small.data"}, |
| "yellow_and_small_or_adult_and_stretch": {"train": "https://huggingface.co/datasets/mstz/balloons/raw/main/yellow-small+adult-stretch.data"} |
| } |
| features_types_per_config = { |
| "adult_or_stretch": { |
| "color": datasets.Value("string"), |
| "size": datasets.Value("string"), |
| "act": datasets.Value("string"), |
| "age": datasets.Value("string"), |
| "is_inflated": datasets.ClassLabel(num_classes=2) |
| }, |
| "adult_and_stretch": { |
| "color": datasets.Value("string"), |
| "size": datasets.Value("string"), |
| "act": datasets.Value("string"), |
| "age": datasets.Value("string"), |
| "is_inflated": datasets.ClassLabel(num_classes=2) |
| }, |
| "yellow_and_small": { |
| "color": datasets.Value("string"), |
| "size": datasets.Value("string"), |
| "act": datasets.Value("string"), |
| "age": datasets.Value("string"), |
| "is_inflated": datasets.ClassLabel(num_classes=2) |
| }, |
| "yellow_and_small_or_adult_and_stretch": { |
| "color": datasets.Value("string"), |
| "size": datasets.Value("string"), |
| "act": datasets.Value("string"), |
| "age": datasets.Value("string"), |
| "is_inflated": datasets.ClassLabel(num_classes=2) |
| } |
| } |
| features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
|
|
|
|
| class BalloonsConfig(datasets.BuilderConfig): |
| def __init__(self, **kwargs): |
| super(BalloonsConfig, self).__init__(version=VERSION, **kwargs) |
| self.features = features_per_config[kwargs["name"]] |
|
|
|
|
| class Balloons(datasets.GeneratorBasedBuilder): |
| |
| DEFAULT_CONFIG = "adult_or_stretch" |
| BUILDER_CONFIGS = [ |
| BalloonsConfig(name="adult_or_stretch", |
| description="Binary classification, balloons are inflated if age == adult or act == stretch."), |
| BalloonsConfig(name="adult_and_stretch", |
| description="Binary classification, balloons are inflated if age == adult and act == stretch."), |
| BalloonsConfig(name="yellow_and_small", |
| description="Binary classification, balloons are inflated if color == yellow and size == small."), |
| BalloonsConfig(name="yellow_and_small_or_adult_and_stretch", |
| description="Binary classification, balloons are inflated if color == yellow and size == small or age == adult and act == stretch.") |
| ] |
|
|
|
|
| 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_per_config = {config: dl_manager.download_and_extract(urls_per_split) for config in urls_per_split} |
| print(downloads_per_config) |
| return [ |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads_per_config[self.config.name][self.config.name]["train"]}) |
| ] |
| |
| def _generate_examples(self, filepath: str): |
| data = pandas.read_csv(filepath, header=None) |
| data.columns = _BASE_FEATURE_NAMES |
| data.loc[:, "is_inflated"] = data.is_inflated.apply(lambda x: 1 if x == "T" else 0) |
|
|
| for row_id, row in data.iterrows(): |
| data_row = dict(row) |
|
|
| yield row_id, data_row |
|
|
