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 *.jpg filter=lfs diff=lfs merge=lfs -text
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+uscensus1990.csv filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,16 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- uscensus
+- tabular_classification
+- binary_classification
+- UCI
+pretty_name: Uscensus
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- uscensus
 ---
+# Uscensus
+[US census dataset]() from [UCI]().

uscensus.py

@@ -0,0 +1,217 @@
+"""Uscensus Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {}
+_BASE_FEATURE_NAMES = [
+	"caseid",
+	"dAge",
+	"dAncstry1",
+	"dAncstry2",
+	"iAvail",
+	"iCitizen",
+	"iClass",
+	"dDepart",
+	"iDisabl1",
+	"iDisabl2",
+	"iEnglish",
+	"iFeb55",
+	"iFertil",
+	"dHispanic",
+	"dHour89",
+	"dHours",
+	"iImmigr",
+	"dIncome1",
+	"dIncome2",
+	"dIncome3",
+	"dIncome4",
+	"dIncome5",
+	"dIncome6",
+	"dIncome7",
+	"dIncome8",
+	"dIndustry",
+	"iKorean",
+	"iLang1",
+	"iLooking",
+	"iMarital",
+	"iMay75880",
+	"iMeans",
+	"iMilitary",
+	"iMobility",
+	"iMobillim",
+	"dOccup",
+	"iOthrserv",
+	"iPerscare",
+	"dPOB",
+	"dPoverty",
+	"dPwgt1",
+	"iRagechld",
+	"dRearning",
+	"iRelat1",
+	"iRelat2",
+	"iRemplpar",
+	"iRiders",
+	"iRlabor",
+	"iRownchld",
+	"dRpincome",
+	"iRPOB",
+	"iRrelchld",
+	"iRspouse",
+	"iRvetserv",
+	"iSchool",
+	"iSept80",
+	"iSex",
+	"iSubfam1",
+	"iSubfam2",
+	"iTmpabsnt",
+	"dTravtime",
+	"iVietnam",
+	"dWeek89",
+	"iWork89",
+	"iWorklwk",
+	"iWWII",
+	"iYearsch",
+	"iYearwrk",
+	"dYrsserv",
+]
+
+DESCRIPTION = "Uscensus dataset."
+_HOMEPAGE = ""
+_URLS = ("")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/uscensus/resolve/main/uscensus.csv"
+}
+features_types_per_config = {
+	"uscensus": {
+		"dAge": datasets.Value("integer"),
+		"dAncstry1": datasets.Value("integer"),
+		"dAncstry2": datasets.Value("integer"),
+		"iAvail": datasets.Value("integer"),
+		"iCitizen": datasets.Value("integer"),
+		"iClass": datasets.Value("integer"),
+		"dDepart": datasets.Value("integer"),
+		"iDisabl1": datasets.Value("integer"),
+		"iDisabl2": datasets.Value("integer"),
+		"iEnglish": datasets.Value("integer"),
+		"iFeb55": datasets.Value("integer"),
+		"iFertil": datasets.Value("integer"),
+		"dHispanic": datasets.Value("integer"),
+		"dHour89": datasets.Value("integer"),
+		"dHours": datasets.Value("integer"),
+		"iImmigr": datasets.Value("integer"),
+		"dIncome1": datasets.Value("integer"),
+		"dIncome2": datasets.Value("integer"),
+		"dIncome3": datasets.Value("integer"),
+		"dIncome4": datasets.Value("integer"),
+		"dIncome5": datasets.Value("integer"),
+		"dIncome6": datasets.Value("integer"),
+		"dIncome7": datasets.Value("integer"),
+		"dIncome8": datasets.Value("integer"),
+		"dIndustry": datasets.Value("integer"),
+		"iKorean": datasets.Value("integer"),
+		"iLang1": datasets.Value("integer"),
+		"iLooking": datasets.Value("integer"),
+		"iMarital": datasets.Value("integer"),
+		"iMay75880": datasets.Value("integer"),
+		"iMeans": datasets.Value("integer"),
+		"iMilitary": datasets.Value("integer"),
+		"iMobility": datasets.Value("integer"),
+		"iMobillim": datasets.Value("integer"),
+		"dOccup": datasets.Value("integer"),
+		"iOthrserv": datasets.Value("integer"),
+		"iPerscare": datasets.Value("integer"),
+		"dPOB": datasets.Value("integer"),
+		"dPoverty": datasets.Value("integer"),
+		"dPwgt1": datasets.Value("integer"),
+		"iRagechld": datasets.Value("integer"),
+		"dRearning": datasets.Value("integer"),
+		"iRelat1": datasets.Value("integer"),
+		"iRelat2": datasets.Value("integer"),
+		"iRemplpar": datasets.Value("integer"),
+		"iRiders": datasets.Value("integer"),
+		"iRlabor": datasets.Value("integer"),
+		"iRownchld": datasets.Value("integer"),
+		"dRpincome": datasets.Value("integer"),
+		"iRPOB": datasets.Value("integer"),
+		"iRrelchld": datasets.Value("integer"),
+		"iRspouse": datasets.Value("integer"),
+		"iRvetserv": datasets.Value("integer"),
+		"iSchool": datasets.Value("integer"),
+		"iSept80": datasets.Value("integer"),
+		"iSex": datasets.Value("integer"),
+		"iSubfam1": datasets.Value("integer"),
+		"iSubfam2": datasets.Value("integer"),
+		"iTmpabsnt": datasets.Value("integer"),
+		"dTravtime": datasets.Value("integer"),
+		"iVietnam": datasets.Value("integer"),
+		"dWeek89": datasets.Value("integer"),
+		"iWork89": datasets.Value("integer"),
+		"iWorklwk": datasets.Value("integer"),
+		"iWWII": datasets.Value("integer"),
+		"iYearsch": datasets.Value("integer"),
+		"iYearwrk": datasets.Value("integer"),
+		"dYrsserv": datasets.ClassLabel(num_classes=3)
+	}
+}
+
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+
+
+class UscensusConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(UscensusConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Uscensus(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "uscensus"
+	BUILDER_CONFIGS = [UscensusConfig(name="uscensus", description="Uscensus 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:
+		data.columns = _BASE_FEATURE_NAMES
+
+		for feature in _ENCODING_DICS:
+			encoding_function = partial(self.encode, feature)
+			data[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}")

uscensus1990.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab706d4d20053f6b15c679a4d56e6836c941f089c1ff4ee67a0c496619d7985b
+size 361343635