balance_scale.py

"""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