vertebral_column.py

"""VertebralColumn"""

from typing import List

import datasets

import pandas


VERSION = datasets.Version("1.0.0")


DESCRIPTION = "VertebralColumn dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/VertebralColumn"
_URLS = ("https://archive.ics.uci.edu/ml/datasets/VertebralColumn")
_CITATION = """
@misc{misc_vertebral_column_212,
  author       = {Barreto,Guilherme & Neto,Ajalmar},
  title        = {{Vertebral Column}},
  year         = {2011},
  howpublished = {UCI Machine Learning Repository},
  note         = {{DOI}: \\url{10.24432/C5K89B}}
}"""

# Dataset info
urls_per_split = {
    "train": "https://huggingface.co/datasets/mstz/vertebral_column/raw/main/data.csv"
}
features_types_per_config = {
    "abnormal": {
	    "pelvic_incidence": datasets.Value("float64"),
		"pelvic_tilt": datasets.Value("float64"),
		"lumbar_lordosis_angle": datasets.Value("float64"),
		"sacral_slope": datasets.Value("float64"),
		"pelvic_radius": datasets.Value("float64"),
		"degree_spondylolisthesis": datasets.Value("float64"),
		"is_abnormal": 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 VertebralColumnConfig(datasets.BuilderConfig):
    def __init__(self, **kwargs):
        super(VertebralColumnConfig, self).__init__(version=VERSION, **kwargs)
        self.features = features_per_config[kwargs["name"]]


class VertebralColumn(datasets.GeneratorBasedBuilder):
    # dataset versions
    DEFAULT_CONFIG = "abnormal"
    BUILDER_CONFIGS = [
        VertebralColumnConfig(name="abnormal",
                    description="VertebralColumn for binary classification.")
        ]


    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)

        for row_id, row in data.iterrows():
            data_row = dict(row)

            yield row_id, data_row