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README.md

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+---
+language:
+- en
+tags:
+- wine
+- tabular_classification
+- binary_classification
+pretty_name: Compas
+size_categories:
+- 1K<n<10K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- wine
+---
+# Wine
+The [Wine dataset](https://www.kaggle.com/datasets/ghassenkhaled/wine-quality-data) is cool.

wine.py

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+"""Wine Dataset"""
+
+from typing import List
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+_BASE_FEATURE_NAMES = [
+    "fixed_acidity",
+    "volatile_acidity",
+    "citric_acid",
+    "residual_sugar",
+    "chlorides",
+    "free_sulfur_dioxide",
+    "total_sulfur_dioxide",
+    "density",
+    "pH",
+    "sulphates",
+    "alcohol",
+    "quality",
+    "color"
+]
+
+
+DESCRIPTION = "Wine quality dataset."
+_HOMEPAGE = "https://www.kaggle.com/datasets/ghassenkhaled/wine-quality-data"
+_URLS = ("https://www.kaggle.com/datasets/ghassenkhaled/wine-quality-data")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/wine/raw/main/Wine_Quality_Data.csv",
+}
+features_types_per_config = {
+    "wine": {
+        "fixed_acidity": datasets.Value("float64"),
+        "volatile_acidity": datasets.Value("float64"),
+        "citric_acid": datasets.Value("float64"),
+        "residual_sugar": datasets.Value("float64"),
+        "chlorides": datasets.Value("float64"),
+        "free_sulfur_dioxide": datasets.Value("float64"),
+        "total_sulfur_dioxide": datasets.Value("float64"),
+        "density": datasets.Value("float64"),
+        "pH": datasets.Value("float64"),
+        "sulphates": datasets.Value("float64"),
+        "alcohol": datasets.Value("float64"),
+        "quality": datasets.Value("int8"),
+        "color": datasets.ClassLabel(num_classes=2, names=("red", "white"))
+    }
+    
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class WineConfig(datasets.BuilderConfig):
+    def __init__(self, **kwargs):
+        super(WineConfig, self).__init__(version=VERSION, **kwargs)
+        self.features = features_per_config[kwargs["name"]]
+
+
+class Wine(datasets.GeneratorBasedBuilder):
+    # dataset versions
+    DEFAULT_CONFIG = "wine"
+    BUILDER_CONFIGS = [
+        WineConfig(name="wine",
+                   description="Binary classification."),
+    ]
+
+
+    def _info(self):
+        if self.config.name not in features_per_config:
+            raise ValueError(f"Unknown configuration: {self.config.name}")
+        
+        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)
+        data = self.preprocess(data, config=self.config.name)
+
+        for row_id, row in data.iterrows():
+            data_row = dict(row)
+
+            yield row_id, data_row
+
+    def preprocess(self, data: pandas.DataFrame, config: str = "wine") -> pandas.DataFrame:
+        data.loc[data.color == "red", "color"] = 0
+        data.loc[data.color == "white", "color"] = 1
+         
+        data.columns = _BASE_FEATURE_NAMES
+        
+        if config == "wine":
+            return data
+        else:
+            raise ValueError(f"Unknown config: {config}")