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.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 dexter.data filter=lfs diff=lfs merge=lfs -text
+gisette.data filter=lfs diff=lfs merge=lfs -text

gisette.data

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9b74d001c8a0bde9d4d1bb56f5bdedc18518947f7d17a5e44b1478584b02be1a
+size 79112638

gisette.py

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+"""Gisette Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {}
+
+DESCRIPTION = "Gisette dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/170/gisette"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/170/gisette")
+_CITATION = """
+@misc{misc_gisette_170,
+  author       = {Guyon,Isabelle, Gunn,Steve, Ben-Hur,Asa & Dror,Gideon},
+  title        = {{Gisette}},
+  year         = {2008},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5HP5B}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/gisette/resolve/main/gisette.data"
+}
+features_types_per_config = {
+	"gisette": {f"feature_{i}": datasets.Value("int64") for i in range(5000)}
+}
+features_types_per_config["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 GisetteConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(GisetteConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Gisette(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "gisette"
+	BUILDER_CONFIGS = [
+		GisetteConfig(name="gisette", description="Gisette for multiclass 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, header=None)
+		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:
+		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}")