Datasets:
File size: 4,383 Bytes
d7411e8 d6672f2 d7411e8 d6672f2 d7411e8 d6672f2 d7411e8 d6672f2 d7411e8 d6672f2 d7411e8 40ee641 d7411e8 35f32a2 d7411e8 b63d705 d7411e8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 |
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}}
}"""
# Dataset info
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):
# dataset versions
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
|