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README.md

@@ -1,3 +1,29 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- wine_origin
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: Wine Origin
+size_categories:
+- n<1K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- wine_origin
+- wine_origin_0
+- wine_origin_1
+- wine_origin_2
 ---
+# Wine Origin
+The [Wine Origin dataset](https://archive-beta.ics.uci.edu/dataset/109/wine) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  | **Description**         |
+|-----------------------|---------------------------|-------------------------|
+| wine_origin                 | Multiclass classification.|                         |
+| wine_origin_0               | Binary classification.    | Is the instance of class 0? |
+| wine_origin_1               | Binary classification.    | Is the instance of class 1? |
+| wine_origin_2               | Binary classification.    | Is the instance of class 2? |

wine.data

@@ -0,0 +1,179 @@
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+13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615,3
+13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520,3
+13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695,3
+12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685,3
+13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750,3
+13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630,3
+12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510,3
+12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470,3
+14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660,3
+13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740,3
+13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750,3
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+14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560,3

wine_origin.py

@@ -0,0 +1,155 @@
+"""WineOrigin Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {}
+
+DESCRIPTION = "WineOrigin dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/108/wine_origin+database+generator+version+2"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/108/wine_origin+database+generator+version+2")
+_CITATION = """
+@misc{misc_wine_origin_database_generator_(version_2)_108,
+  author       = {Breiman,L. & Stone,C.J.},
+  title        = {{Waveform Database Generator (Version 2)}},
+  year         = {1988},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C56014}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/wine_origin/raw/main/wine.data"
+}
+features_types_per_config = {
+	"wine_origin": {
+		"malic_acid": datasets.Value("float64"),
+		"ash": datasets.Value("float64"),
+		"alcalinity_of_ash": datasets.Value("float64"),
+		"magnesium": datasets.Value("float64"),
+		"phenols": datasets.Value("float64"),
+		"flavanoids": datasets.Value("float64"),
+		"nonflavanoid_phenols": datasets.Value("float64"),
+		"proanthocyanins": datasets.Value("float64"),
+		"color_intensity": datasets.Value("float64"),
+		"hue": datasets.Value("float64"),
+		"diluted_wines": datasets.Value("float64"),
+		"proline": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=3)
+	},
+	"wine_origin_0": {
+		"malic_acid": datasets.Value("float64"),
+		"ash": datasets.Value("float64"),
+		"alcalinity_of_ash": datasets.Value("float64"),
+		"magnesium": datasets.Value("float64"),
+		"phenols": datasets.Value("float64"),
+		"flavanoids": datasets.Value("float64"),
+		"nonflavanoid_phenols": datasets.Value("float64"),
+		"proanthocyanins": datasets.Value("float64"),
+		"color_intensity": datasets.Value("float64"),
+		"hue": datasets.Value("float64"),
+		"diluted_wines": datasets.Value("float64"),
+		"proline": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"wine_origin_1": {
+		"malic_acid": datasets.Value("float64"),
+		"ash": datasets.Value("float64"),
+		"alcalinity_of_ash": datasets.Value("float64"),
+		"magnesium": datasets.Value("float64"),
+		"phenols": datasets.Value("float64"),
+		"flavanoids": datasets.Value("float64"),
+		"nonflavanoid_phenols": datasets.Value("float64"),
+		"proanthocyanins": datasets.Value("float64"),
+		"color_intensity": datasets.Value("float64"),
+		"hue": datasets.Value("float64"),
+		"diluted_wines": datasets.Value("float64"),
+		"proline": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"wine_origin_2": {
+		"malic_acid": datasets.Value("float64"),
+		"ash": datasets.Value("float64"),
+		"alcalinity_of_ash": datasets.Value("float64"),
+		"magnesium": datasets.Value("float64"),
+		"phenols": datasets.Value("float64"),
+		"flavanoids": datasets.Value("float64"),
+		"nonflavanoid_phenols": datasets.Value("float64"),
+		"proanthocyanins": datasets.Value("float64"),
+		"color_intensity": datasets.Value("float64"),
+		"hue": datasets.Value("float64"),
+		"diluted_wines": datasets.Value("float64"),
+		"proline": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class WineOriginConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(WineOriginConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class WineOrigin(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "wine_origin"
+	BUILDER_CONFIGS = [
+		WineOriginConfig(name="wine_origin", description="WineOrigin for multiclass classification."),
+		WineOriginConfig(name="wine_origin_0", description="WineOrigin for binary classification."),
+		WineOriginConfig(name="wine_origin_1", description="WineOrigin for binary classification."),
+		WineOriginConfig(name="wine_origin_2", description="WineOrigin 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)
+		data = self.preprocess(data)
+
+		for row_id, row in data.iterrows():
+			data_row = dict(row)
+
+			yield row_id, data_row
+
+	def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
+		if self.config.name == "wine_origin_0":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
+		elif self.config.name == "wine_origin_1":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
+		elif self.config.name == "wine_origin_2":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
+
+		for feature in _ENCODING_DICS:
+			encoding_function = partial(self.encode, feature)
+			data.loc[:, feature] = data[feature].apply(encoding_function)
+				
+		return data[list(features_types_per_config[self.config.name].keys())]
+
+	def encode(self, feature, value):
+		if feature in _ENCODING_DICS:
+			return _ENCODING_DICS[feature][value]
+		raise ValueError(f"Unknown feature: {feature}")