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README.md

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 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- p53
+- tabular_classification
+- binary_classification
+pretty_name: P53
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- p53
 ---
+# P53
+The [P53 dataset](https://archive-beta.ics.uci.edu/dataset/170/p53) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  | **Description**         |
+|-----------------------|---------------------------|-------------------------|
+| p53                   | Binary classification.|                         |

p53.data

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b86a54915a56eef6b256c8567b8e2cd1127b2137f1cd59c4437af80d99d729f7
+size 1157416390

p53.py

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+"""P53 Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {
+	"class": {
+		"inactive": 0,
+		"active": 1
+	}
+}
+
+DESCRIPTION = "P53 dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/170/p53"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/170/p53")
+_CITATION = """
+@misc{misc_p53_mutants_188,
+  author       = {Lathrop,Richard},
+  title        = {{p53 Mutants}},
+  year         = {2010},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5T89H}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/p53/resolve/main/p53.data"
+}
+features_types_per_config = {
+	"p53": {f"feature_{i}": datasets.Value("float64") for i in range(5408)}
+}
+features_types_per_config["p53"]["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 P53Config(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(P53Config, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class P53(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "p53"
+	BUILDER_CONFIGS = [
+		P53Config(name="p53", description="P53 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, 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)
+		
+		for feature in list(data.colums)[:-1]:
+			data[data[feature] == "?", feature] = data[feature].mean()
+				
+		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}")