updated to datasets 4.*

README.md

@@ -1,18 +1,20 @@
 ---
-language:
-- en
+configs:
+- config_name: wine
+  data_files:
+  - path: wine/train.csv
+    split: train
+  default: true
+language: en
+license: cc
+pretty_name: Wine
+size_categories: 1M<n<10M
 tags:
-- wine
 - tabular_classification
 - binary_classification
-pretty_name: Wine quality
-size_categories:
-- 1K<n<10K
+- multiclass_classification
 task_categories:
 - tabular-classification
-configs:
-- wine
-license: cc
 ---
 # Wine
 The [Wine dataset](https://www.kaggle.com/datasets/ghassenkhaled/wine-quality-data) from Kaggle.

wine.py

@@ -1,101 +0,0 @@
-"""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",
-    "is_red"
-]
-
-
-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"),
-        "is_red": 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):
-        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 = DEFAULT_CONFIG) -> pandas.DataFrame:
-        data = data.rename(columns={"color": "is_red"})       
-        data.loc[data.is_red == "red", "is_red"] = 0
-        data.loc[data.is_red == "white", "is_red"] = 1
-        
-        return data
-