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+pums.csv filter=lfs diff=lfs merge=lfs -text

README.md

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 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- pums
+- tabular_classification
+- binary_classification
+- UCI
+pretty_name: Ipums
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- pums
 ---
+# Pums
+The [Pums dataset](https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990) from the [UCI repository](https://archive-beta.ics.uci.edu/).
+U.S.A. Census dataset, classify the income of the individual.
+
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  |
+|-----------------------|---------------------------|
+| pums          | Binary classification.|

pums.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6905c75708458f5a1a8809e19a5e3aae21549f964edbe06ebc6b1dab3e6aac6d
+size 141527293

pums.py

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+"""Pums Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {
+	"class": {
+		"- 50000.": 0,
+		"50000+.": 1
+	}
+}
+
+DESCRIPTION = "Pums dataset."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/116/us+census+data+1990")
+_CITATION = """
+@misc{misc_us_census_data_(1990)_116,
+  author       = {Meek,Meek, Thiesson,Thiesson & Heckerman,Heckerman},
+  title        = {{US Census Data (1990)}},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C5VP42}}
+}
+"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/pums/resolve/main/pums.csv"
+}
+features_types_per_config = {
+	"pums": {
+		"age": datasets.Value("int64"),
+		"class_of_worker": datasets.Value("string"),
+		"detailed_industry_recode": datasets.Value("string"),
+		"detailed_occupation_recode": datasets.Value("string"),
+		"education": datasets.Value("string"),
+		"wage_per_hour": datasets.Value("int64"),
+		"enroll_in_edu_inst_last_wk": datasets.Value("string"),
+		"marital_stat": datasets.Value("string"),
+		"major_industry_code": datasets.Value("string"),
+		"major_occupation_code": datasets.Value("string"),
+		"race": datasets.Value("string"),
+		"hispanic_origin": datasets.Value("string"),
+		"sex": datasets.Value("string"),
+		"member_of_a_labor_union": datasets.Value("string"),
+		"reason_for_unemployment": datasets.Value("string"),
+		"full_or_part_time_employment_stat": datasets.Value("string"),
+		"capital_gains": datasets.Value("int64"),
+		"capital_losses": datasets.Value("int64"),
+		"dividends_from_stocks": datasets.Value("int64"),
+		"tax_filer_stat": datasets.Value("string"),
+		"region_of_previous_residence": datasets.Value("string"),
+		"state_of_previous_residence": datasets.Value("string"),
+		"detailed_household_and_family_stat": datasets.Value("string"),
+		"detailed_household_summary_in_household": datasets.Value("string"),
+		# "instance_weight": datasets.Value("int64"),
+		"migration_code_change_in_msa": datasets.Value("string"),
+		"migration_code_change_in_reg": datasets.Value("string"),
+		"migration_code_move_within_reg": datasets.Value("string"),
+		"live_in_this_house_1_year_ago": datasets.Value("string"),
+		"migration_prev_res_in_sunbelt": datasets.Value("string"),
+		"num_persons_worked_for_employer": datasets.Value("int64"),
+		"family_members_under_18": datasets.Value("string"),
+		"country_of_birth_father": datasets.Value("string"),
+		"country_of_birth_mother": datasets.Value("string"),
+		"country_of_birth_self": datasets.Value("string"),
+		"citizenship": datasets.Value("string"),
+		"own_business_or_self_employed": datasets.Value("string"),
+		"fill_inc_questionnaire_for_veteran_admin": datasets.Value("string"),
+		"veterans_benefits": datasets.Value("string"),
+		"weeks_worked_in_year": datasets.Value("int64"),
+		"year": 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 PumsConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(PumsConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Pums(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "pums"
+	BUILDER_CONFIGS = [PumsConfig(name="pums", description="Pums 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)
+		
+		data.drop("instance_weight", axis="columns", inplace=True)
+		data = data.rename(columns={"instance migration_code_change_in_msa": "migration_code_change_in_msa"})
+				
+		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}")