Upload 3 files

README.md

@@ -1,3 +1,43 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- yeast
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: Yeast
+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:
+- yeast
+- yeast_0
+- yeast_1
+- yeast_2
+- yeast_3
+- yeast_4
+- yeast_5
+- yeast_6
+- yeast_7
+- yeast_8
+- yeast_9
 ---
+# Yeast
+The [Yeast dataset](https://archive-beta.ics.uci.edu/dataset/110/yeast) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  | **Description**         |
+|-----------------------|---------------------------|-------------------------|
+| wine_origin           | Multiclass classification.|                         |
+| yeast_0               | Binary classification.    | Is the instance of class 0? |
+| yeast_1               | Binary classification.    | Is the instance of class 1? |
+| yeast_2               | Binary classification.    | Is the instance of class 2? |
+| yeast_3               | Binary classification.    | Is the instance of class 3? |
+| yeast_4               | Binary classification.    | Is the instance of class 4? |
+| yeast_5               | Binary classification.    | Is the instance of class 5? |
+| yeast_6               | Binary classification.    | Is the instance of class 6? |
+| yeast_7               | Binary classification.    | Is the instance of class 7? |
+| yeast_8               | Binary classification.    | Is the instance of class 8? |
+| yeast_9               | Binary classification.    | Is the instance of class 9? |

yeast.csv

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yeast.py

@@ -0,0 +1,250 @@
+"""Yeast Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {
+	"class": {
+		"MIT": 0,
+		"NUC": 1,
+		"CYT": 2,
+		"ME1": 3,
+		"EXC": 4,
+		"ME2": 5,
+		"ME3": 6,
+		"VAC": 7,
+		"POX": 8,
+		"ERL": 9
+	}
+}
+
+DESCRIPTION = "Yeast dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/110/yeast"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/110/yeast")
+_CITATION = """
+@misc{misc_yeast_110,
+  author       = {Nakai,Kenta},
+  title        = {{Yeast}},
+  year         = {1996},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5KG68}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/yeast/raw/main/yeast.csv"
+}
+features_types_per_config = {
+	"yeast": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=10)
+	},
+	"yeast_0": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"yeast_1": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"yeast_2": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"yeast_3": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"yeast_4": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"yeast_5": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"yeast_6": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"yeast_7": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"yeast_8": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": datasets.Value("float64"),
+		"class": datasets.ClassLabel(num_classes=2)
+	},
+	"yeast_9": {
+		"mcg": datasets.Value("float64"),
+		"gvh": datasets.Value("float64"),
+		"alm": datasets.Value("float64"),
+		"mit": datasets.Value("float64"),
+		"erl": datasets.Value("float64"),
+		"pox": datasets.Value("float64"),
+		"vac": datasets.Value("float64"),
+		"nuc": 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 YeastConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(YeastConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Yeast(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "yeast"
+	BUILDER_CONFIGS = [
+		YeastConfig(name="yeast", description="Yeast for multiclass classification."),
+		YeastConfig(name="yeast_0", description="Yeast for binary classification."),
+		YeastConfig(name="yeast_1", description="Yeast for binary classification."),
+		YeastConfig(name="yeast_2", description="Yeast for binary classification."),
+		YeastConfig(name="yeast_3", description="Yeast for binary classification."),
+		YeastConfig(name="yeast_4", description="Yeast for binary classification."),
+		YeastConfig(name="yeast_5", description="Yeast for binary classification."),
+		YeastConfig(name="yeast_6", description="Yeast for binary classification."),
+		YeastConfig(name="yeast_7", description="Yeast for binary classification."),
+		YeastConfig(name="yeast_8", description="Yeast for binary classification."),
+		YeastConfig(name="yeast_9", description="Yeast 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:
+		for feature in _ENCODING_DICS:
+			encoding_function = partial(self.encode, feature)
+			data.loc[:, feature] = data[feature].apply(encoding_function)
+
+		if self.config.name == "yeast_0":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
+		elif self.config.name == "yeast_1":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
+		elif self.config.name == "yeast_2":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
+		elif self.config.name == "yeast_3":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
+		elif self.config.name == "yeast_4":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
+		elif self.config.name == "yeast_5":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
+		elif self.config.name == "yeast_6":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 6 else 0)
+		elif self.config.name == "yeast_7":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 7 else 0)
+		elif self.config.name == "yeast_8":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 8 else 0)
+		elif self.config.name == "yeast_9":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 9 else 0)
+				
+		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}")