Upload 3 files

README.md

@@ -1,3 +1,26 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- optdigits
+- tabular_classification
+- binary_classification
+- multiclass_classification
+- UCI
+pretty_name: Optdigits
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- optdigits
 ---
+# Optdigits
+The [Optdigits dataset](https://archive-beta.ics.uci.edu/dataset/80/optical+recognition+of+handwritten+digits) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  | **Description**         |
+|-----------------------|---------------------------|-------------------------|
+| optdigits             | Multiclass classification.|                         |
+| 0                     | Binary classification.    | Is this a 0?            |
+| 1                     | Binary classification.    | Is this a 1?            |
+| 2                     | Binary classification.    | Is this a 2?            |
+| ...                   | Binary classification.    | ...                     |

optdigits.py

@@ -0,0 +1,321 @@
+"""Optdigits Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {}
+_BASE_FEATURE_NAMES = [
+	"att1",
+	"att2",
+	"att3",
+	"att4",
+	"att5",
+	"att6",
+	"att7",
+	"att8",
+	"att9",
+	"att10",
+	"att11",
+	"att12",
+	"att13",
+	"att14",
+	"att15",
+	"att16",
+	"att17",
+	"att18",
+	"att19",
+	"att20",
+	"att21",
+	"att22",
+	"att23",
+	"att24",
+	"att25",
+	"att26",
+	"att27",
+	"att28",
+	"att29",
+	"att30",
+	"att31",
+	"att32",
+	"att33",
+	"att34",
+	"att35",
+	"att36",
+	"att37",
+	"att38",
+	"att39",
+	"att40",
+	"att41",
+	"att42",
+	"att43",
+	"att44",
+	"att45",
+	"att46",
+	"att47",
+	"att48",
+	"att49",
+	"att50",
+	"att51",
+	"att52",
+	"att53",
+	"att54",
+	"att55",
+	"att56",
+	"att57",
+	"att58",
+	"att59",
+	"att60",
+	"att61",
+	"att62",
+	"att63",
+	"att64",
+	"class",
+]
+
+DESCRIPTION = "Optdigits dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/80/optical+recognition+of+handwritten+digits"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/80/optical+recognition+of+handwritten+digits")
+_CITATION = """
+@misc{misc_optical_recognition_of_handwritten_digits_80,
+  author       = {Alpaydin,E. & Kaynak,C.},
+  title        = {{Optical Recognition of Handwritten Digits}},
+  year         = {1998},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C50P49}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/optdigits/resolve/main/optdigits.data"
+}
+features_types_per_config = {
+	"optdigits": {
+		"att1": datasets.Value("int64"),
+		"att2": datasets.Value("int64"),
+		"att3": datasets.Value("int64"),
+		"att4": datasets.Value("int64"),
+		"att5": datasets.Value("int64"),
+		"att6": datasets.Value("int64"),
+		"att7": datasets.Value("int64"),
+		"att8": datasets.Value("int64"),
+		"att9": datasets.Value("int64"),
+		"att10": datasets.Value("int64"),
+		"att11": datasets.Value("int64"),
+		"att12": datasets.Value("int64"),
+		"att13": datasets.Value("int64"),
+		"att14": datasets.Value("int64"),
+		"att15": datasets.Value("int64"),
+		"att16": datasets.Value("int64"),
+		"att17": datasets.Value("int64"),
+		"att18": datasets.Value("int64"),
+		"att19": datasets.Value("int64"),
+		"att20": datasets.Value("int64"),
+		"att21": datasets.Value("int64"),
+		"att22": datasets.Value("int64"),
+		"att23": datasets.Value("int64"),
+		"att24": datasets.Value("int64"),
+		"att25": datasets.Value("int64"),
+		"att26": datasets.Value("int64"),
+		"att27": datasets.Value("int64"),
+		"att28": datasets.Value("int64"),
+		"att29": datasets.Value("int64"),
+		"att30": datasets.Value("int64"),
+		"att31": datasets.Value("int64"),
+		"att32": datasets.Value("int64"),
+		"att33": datasets.Value("int64"),
+		"att34": datasets.Value("int64"),
+		"att35": datasets.Value("int64"),
+		"att36": datasets.Value("int64"),
+		"att37": datasets.Value("int64"),
+		"att38": datasets.Value("int64"),
+		"att39": datasets.Value("int64"),
+		"att40": datasets.Value("int64"),
+		"att41": datasets.Value("int64"),
+		"att42": datasets.Value("int64"),
+		"att43": datasets.Value("int64"),
+		"att44": datasets.Value("int64"),
+		"att45": datasets.Value("int64"),
+		"att46": datasets.Value("int64"),
+		"att47": datasets.Value("int64"),
+		"att48": datasets.Value("int64"),
+		"att49": datasets.Value("int64"),
+		"att50": datasets.Value("int64"),
+		"att51": datasets.Value("int64"),
+		"att52": datasets.Value("int64"),
+		"att53": datasets.Value("int64"),
+		"att54": datasets.Value("int64"),
+		"att55": datasets.Value("int64"),
+		"att56": datasets.Value("int64"),
+		"att57": datasets.Value("int64"),
+		"att58": datasets.Value("int64"),
+		"att59": datasets.Value("int64"),
+		"att60": datasets.Value("int64"),
+		"att61": datasets.Value("int64"),
+		"att62": datasets.Value("int64"),
+		"att63": datasets.Value("int64"),
+		"att64": datasets.Value("int64"),
+		"class": datasets.ClassLabel(num_classes=10)
+	}
+}
+for i in range(10):
+	features_types_per_config[str(i)] = {
+		"att1": datasets.Value("int64"),
+		"att2": datasets.Value("int64"),
+		"att3": datasets.Value("int64"),
+		"att4": datasets.Value("int64"),
+		"att5": datasets.Value("int64"),
+		"att6": datasets.Value("int64"),
+		"att7": datasets.Value("int64"),
+		"att8": datasets.Value("int64"),
+		"att9": datasets.Value("int64"),
+		"att10": datasets.Value("int64"),
+		"att11": datasets.Value("int64"),
+		"att12": datasets.Value("int64"),
+		"att13": datasets.Value("int64"),
+		"att14": datasets.Value("int64"),
+		"att15": datasets.Value("int64"),
+		"att16": datasets.Value("int64"),
+		"att17": datasets.Value("int64"),
+		"att18": datasets.Value("int64"),
+		"att19": datasets.Value("int64"),
+		"att20": datasets.Value("int64"),
+		"att21": datasets.Value("int64"),
+		"att22": datasets.Value("int64"),
+		"att23": datasets.Value("int64"),
+		"att24": datasets.Value("int64"),
+		"att25": datasets.Value("int64"),
+		"att26": datasets.Value("int64"),
+		"att27": datasets.Value("int64"),
+		"att28": datasets.Value("int64"),
+		"att29": datasets.Value("int64"),
+		"att30": datasets.Value("int64"),
+		"att31": datasets.Value("int64"),
+		"att32": datasets.Value("int64"),
+		"att33": datasets.Value("int64"),
+		"att34": datasets.Value("int64"),
+		"att35": datasets.Value("int64"),
+		"att36": datasets.Value("int64"),
+		"att37": datasets.Value("int64"),
+		"att38": datasets.Value("int64"),
+		"att39": datasets.Value("int64"),
+		"att40": datasets.Value("int64"),
+		"att41": datasets.Value("int64"),
+		"att42": datasets.Value("int64"),
+		"att43": datasets.Value("int64"),
+		"att44": datasets.Value("int64"),
+		"att45": datasets.Value("int64"),
+		"att46": datasets.Value("int64"),
+		"att47": datasets.Value("int64"),
+		"att48": datasets.Value("int64"),
+		"att49": datasets.Value("int64"),
+		"att50": datasets.Value("int64"),
+		"att51": datasets.Value("int64"),
+		"att52": datasets.Value("int64"),
+		"att53": datasets.Value("int64"),
+		"att54": datasets.Value("int64"),
+		"att55": datasets.Value("int64"),
+		"att56": datasets.Value("int64"),
+		"att57": datasets.Value("int64"),
+		"att58": datasets.Value("int64"),
+		"att59": datasets.Value("int64"),
+		"att60": datasets.Value("int64"),
+		"att61": datasets.Value("int64"),
+		"att62": datasets.Value("int64"),
+		"att63": datasets.Value("int64"),
+		"att64": datasets.Value("int64"),
+		"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 OptdigitsConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(OptdigitsConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Optdigits(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "optdigits"
+	BUILDER_CONFIGS = [
+		OptdigitsConfig(name="optdigits", description="Optdigits for multiclass classification."),
+		OptdigitsConfig(name="0", description="Optdigits for binary classification: is this a 0?."),
+		OptdigitsConfig(name="1", description="Optdigits for binary classification: is this a 1?."),
+		OptdigitsConfig(name="2", description="Optdigits for binary classification: is this a 2?."),
+		OptdigitsConfig(name="3", description="Optdigits for binary classification: is this a 3?."),
+		OptdigitsConfig(name="4", description="Optdigits for binary classification: is this a 4?."),
+		OptdigitsConfig(name="5", description="Optdigits for binary classification: is this a 5?."),
+		OptdigitsConfig(name="6", description="Optdigits for binary classification: is this a 6?."),
+		OptdigitsConfig(name="7", description="Optdigits for binary classification: is this a 7?."),
+		OptdigitsConfig(name="8", description="Optdigits for binary classification: is this a 8?."),
+		OptdigitsConfig(name="9", description="Optdigits for binary classification: is this a 9?.")
+	]
+
+
+	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.columns = _BASE_FEATURE_NAMES
+		
+		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 == "0":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
+		if self.config.name == "1":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
+		if self.config.name == "2":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
+		if self.config.name == "3":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
+		if self.config.name == "4":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
+		if self.config.name == "5":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
+		if self.config.name == "6":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 6 else 0)
+		if self.config.name == "7":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 7 else 0)
+		if self.config.name == "8":
+			data["class"] = data["class"].apply(lambda x: 1 if x == 8 else 0)
+		if self.config.name == "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}")