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"""Wine Dataset""" |
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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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"fixed_acidity", |
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"volatile_acidity", |
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"citric_acid", |
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"residual_sugar", |
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"chlorides", |
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"free_sulfur_dioxide", |
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"total_sulfur_dioxide", |
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"density", |
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"pH", |
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"sulphates", |
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"alcohol", |
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"quality", |
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"is_red" |
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] |
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DESCRIPTION = "Wine quality dataset." |
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_HOMEPAGE = "https://www.kaggle.com/datasets/ghassenkhaled/wine-quality-data" |
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_URLS = ("https://www.kaggle.com/datasets/ghassenkhaled/wine-quality-data") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/wine/raw/main/Wine_Quality_Data.csv", |
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} |
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features_types_per_config = { |
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"wine": { |
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"fixed_acidity": datasets.Value("float64"), |
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"volatile_acidity": datasets.Value("float64"), |
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"citric_acid": datasets.Value("float64"), |
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"residual_sugar": datasets.Value("float64"), |
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"chlorides": datasets.Value("float64"), |
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"free_sulfur_dioxide": datasets.Value("float64"), |
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"total_sulfur_dioxide": datasets.Value("float64"), |
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"density": datasets.Value("float64"), |
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"pH": datasets.Value("float64"), |
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"sulphates": datasets.Value("float64"), |
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"alcohol": datasets.Value("float64"), |
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"quality": datasets.Value("int8"), |
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"is_red": datasets.ClassLabel(num_classes=2, names=("red", "white")) |
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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 WineConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(WineConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Wine(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "wine" |
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BUILDER_CONFIGS = [ |
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WineConfig(name="wine", |
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description="Binary 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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] |
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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 = data.rename(columns={"color": "is_red"}) |
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data.loc[data.is_red == "red", "is_red"] = 0 |
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data.loc[data.is_red == "white", "is_red"] = 1 |
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return data |
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