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from typing import List |
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import datasets |
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import pandas |
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VERSION = datasets.Version("1.0.0") |
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_BASE_FEATURE_NAMES = [ |
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"family", |
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"product_type", |
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"steel_type", |
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"carbon", |
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"hardness", |
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"temper_rolling", |
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"condition", |
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"formability", |
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"strength", |
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"non_ageing", |
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"surface_finish", |
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"surface_quality", |
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"enamelability", |
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"bc", |
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"bf", |
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"bt", |
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"bw_time", |
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"bl", |
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"m", |
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"chrom", |
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"phos", |
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"cbond", |
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"marvi", |
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"exptl", |
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"ferro", |
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"corr", |
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"blue", |
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"lustre", |
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"jurofm", |
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"s", |
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"p", |
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"is_coil", |
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"thick", |
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"width", |
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"len", |
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"oil", |
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"bore", |
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"packing", |
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"class" |
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] |
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DESCRIPTION = "Anneal dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/3/annealing" |
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_URLS = ("https://archive-beta.ics.uci.edu/dataset/3/annealing") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/anneal/raw/main/anneal.data", |
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"test": "https://huggingface.co/datasets/mstz/anneal/raw/main/anneal.test" |
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} |
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features_types_per_config = { |
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"annealing": { |
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"family": datasets.Value("string"), |
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"steel_type": datasets.Value("string"), |
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"carbon": datasets.Value("float64"), |
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"hardness": datasets.Value("float64"), |
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"temper_rolling": datasets.Value("bool"), |
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"condition": datasets.Value("string"), |
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"formability": datasets.Value("int8"), |
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"strength": datasets.Value("float64"), |
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"surface_finish": datasets.Value("string"), |
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"surface_quality": datasets.Value("string"), |
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"enamelability": datasets.Value("int8"), |
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"bc": datasets.Value("bool"), |
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"bf": datasets.Value("bool"), |
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"bt": datasets.Value("bool"), |
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"bw_time": datasets.Value("string"), |
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"bl": datasets.Value("bool"), |
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"chrom": datasets.Value("bool"), |
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"phos": datasets.Value("bool"), |
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"cbond": datasets.Value("bool"), |
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"marvi": datasets.Value("bool"), |
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"exptl": datasets.Value("bool"), |
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"ferro": datasets.Value("bool"), |
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"corr": datasets.Value("bool"), |
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"blue": datasets.Value("string"), |
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"lustre": datasets.Value("bool"), |
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"jurofm": datasets.Value("bool"), |
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"s": datasets.Value("bool"), |
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"p": datasets.Value("bool"), |
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"is_coil": datasets.Value("bool"), |
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"thick": datasets.Value("float64"), |
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"width": datasets.Value("float64"), |
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"len": datasets.Value("float64"), |
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"oil": datasets.Value("string"), |
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"bore": datasets.Value("string"), |
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"packing": datasets.Value("string"), |
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"class": datasets.ClassLabel(num_classes=6, names=["1", "2", "3", "4", "5", "U"]) |
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} |
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} |
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
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class AnnealConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(AnnealConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Anneal(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "annealing" |
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BUILDER_CONFIGS = [ |
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AnnealConfig(name="annealing", |
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description="Anneal for multiclass classification.") |
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] |
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def _info(self): |
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
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features=features_per_config[self.config.name]) |
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return info |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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downloads = dl_manager.download_and_extract(urls_per_split) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloads["test"]}) |
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] |
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def _generate_examples(self, filepath: str): |
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data = pandas.read_csv(filepath) |
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data = self.preprocess(data, config=self.config.name) |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
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data.columns = _BASE_FEATURE_NAMES |
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data.drop("product_type", axis="columns", inplace=True) |
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data.drop("non_ageing", axis="columns", inplace=True) |
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data.drop("m", axis="columns", inplace=True) |
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data.loc[:, "formability"] = data.formability.apply(lambda x: int(x) if x not in ("?", "-") else 0) |
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data.loc[:, "enamelability"] = data.enamelability.apply(lambda x: int(x) if x not in ("?", "-") else 0) |
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data.loc[:, "bc"] = data.bc.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "bf"] = data.bf.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "bt"] = data.bt.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "bl"] = data.bl.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "chrom"] = data.chrom.apply(lambda x: True if x == "C" else False) |
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data.loc[:, "phos"] = data.phos.apply(lambda x: True if x == "P" else False) |
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data.loc[:, "cbond"] = data.cbond.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "marvi"] = data.marvi.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "exptl"] = data.exptl.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "ferro"] = data.ferro.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "corr"] = data["corr"].apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "lustre"] = data.lustre.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "jurofm"] = data.jurofm.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "s"] = data.s.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "p"] = data.p.apply(lambda x: True if x == "Y" else False) |
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data.loc[:, "class"] = data.p.apply(lambda x: int(x) if x != "U" else 0) |
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return data |
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