Datasets:
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from typing import List
from functools import partial
import datasets
import pandas
VERSION = datasets.Version("1.0.0")
_ORIGINAL_FEATURE_NAMES = [
"rads",
"age",
"lesion_shape",
"margin",
"density",
"is_severe"
]
_BASE_FEATURE_NAMES = [
"age",
"lesion_shape",
"margin",
"density",
"is_severe"
]
_ENCODING_DICS = {
"lesion_shape": {
"1": "round",
"2": "oval",
"3": "lobular",
"4": "irregular",
},
"margin": {
"1": "circumbscribed",
"2": "microlobulated",
"3": "obscured",
"4": "ill-defined",
"5": "spiculated",
},
"density": {
"1": "high",
"2": "iso",
"3": "low",
"4": "fat-containing",
"5": "spiculated",
}
}
DESCRIPTION = "Mammography dataset from the UCI ML repository."
_HOMEPAGE = "https://archive.ics.uci.edu/ml/datasets/Mammography"
_URLS = ("https://huggingface.co/datasets/mstz/mammography/raw/mammography_masses.data")
_CITATION = """
@misc{misc_mammographic_mass_161,
author = {Elter,Matthias},
title = {{Mammographic Mass}},
year = {2007},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: \\url{10.24432/C53K6Z}}
}"""
# Dataset info
urls_per_split = {
"train": "https://huggingface.co/datasets/mstz/mammography/raw/main/mammographic_masses.data"
}
features_types_per_config = {
"mammography": {
"age": datasets.Value("int8"),
"lesion_shape": datasets.Value("string"),
"margin": datasets.Value("string"),
"density": datasets.Value("string"),
"is_severe": 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 MammographyConfig(datasets.BuilderConfig):
def __init__(self, **kwargs):
super(MammographyConfig, self).__init__(version=VERSION, **kwargs)
self.features = features_per_config[kwargs["name"]]
class Mammography(datasets.GeneratorBasedBuilder):
# dataset versions
DEFAULT_CONFIG = "mammography"
BUILDER_CONFIGS = [
MammographyConfig(name="mammography",
description="Mammography 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, header=None)
data.columns = _ORIGINAL_FEATURE_NAMES
data.drop("rads", axis="columns", inplace=True)
data = data[data.age != "?"]
data = data[data.lesion_shape != "?"]
data = data[data.margin != "?"]
data = data[data.density != "?"]
data = data.infer_objects()
for feature in _ENCODING_DICS:
encoding_function = partial(self.encode, feature)
data.loc[:, feature] = data[feature].apply(encoding_function)
for row_id, row in data.iterrows():
data_row = dict(row)
yield row_id, data_row
def encode(self, feature, value):
if feature in _ENCODING_DICS:
return _ENCODING_DICS[feature][value]
raise ValueError(f"Unknown feature: {feature}")
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