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"""Balance_Scale"""
from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_BASE_FEATURE_NAMES = [
"balance",
"left_weight",
"left_distance",
"right_weight",
"right_distance",
]
DESCRIPTION = "Balance_Scale dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Balance_Scale"
_URLS = ("https://huggingface.co/datasets/mstz/balance_scale/raw/balance_scale.data")
_CITATION = """
@misc{misc_balance_scale_12,
title = {{Balance Scale}},
year = {1994},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C5488X}}
}"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/balance_scale/raw/main/balance_scale.data",
}
features_types_per_config = {
"balance": {
"left_weight": datasets.Value("int64"),
"left_distance": datasets.Value("int64"),
"right_weight": datasets.Value("int64"),
"right_distance": datasets.Value("int64"),
"balance": datasets.ClassLabel(num_classes=3, names=("tips_left", "balanced", "tips_right"))
},
"is_balanced": {
"left_weight": datasets.Value("int64"),
"left_distance": datasets.Value("int64"),
"right_weight": datasets.Value("int64"),
"right_distance": datasets.Value("int64"),
"is_balanced": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
},
}
features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
class Balance_ScaleConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(Balance_ScaleConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Balance_Scale(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "balance"
BUILDER_CONFIGS = [
Balance_ScaleConfig(name="balance", description="Multiclass classification of the scale balance."),
Balance_ScaleConfig(name="is_balanced", description="Binary classification of the scale balance."),
]
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)
data.columns = _BASE_FEATURE_NAMES
data = data[["left_weight", "left_distance", "right_weight", "right_distance", "balance"]]
data["balance"] = data.balance.apply(lambda x: 0 if x == "L" else 1 if x == "B" else 2)
if self.config.name == "is_balanced":
data["balance"] = data.balance.apply(lambda x: 1 if x == 1 else 0)
data = data.rename(columns={"balance": "is_balanced"})
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
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