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.gitattributes

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

README.md

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+---
+language:
+- en
+tags:
+- sydt
+- tabular_classification
+- binary_classification
+- synthetic
+pretty_name: Sydt
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- sydt
+---
+# Sydt
+Synthetic dataset.

sydt.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:82b79cfcfaaabb164e53d2c5d9291cadaee837b2f218311bdd368e12baeb9351
+size 369620598

sydt.py

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+"""Sydt Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {}
+_BASE_FEATURE_NAMES = [
+	"salary",
+	"commission",
+	"age",
+	"education",
+	"car",
+	"zip",
+	"housevalue",
+	"yearsowned",
+	"loan",
+	"class",
+]
+
+DESCRIPTION = "Sydt dataset."
+_HOMEPAGE = ""
+_URLS = ("")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/sydt/resolve/main/sydt.csv"
+}
+features_types_per_config = {
+	"sydt": {
+		"salary": datasets.Value("int64"),
+		"commission": datasets.Value("int64"),
+		"age": datasets.Value("int64"),
+		"education": datasets.Value("int64"),
+		"car": datasets.Value("int64"),
+		"zip": datasets.Value("string"),
+		"housevalue": datasets.Value("int64"),
+		"yearsowned": datasets.Value("int64"),
+		"loan": 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 SydtConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(SydtConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Sydt(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "sydt"
+	BUILDER_CONFIGS = [SydtConfig(name="sydt", description="Sydt 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
+		data = data[~data["class"].isna()]
+		data["class"] = data["class"].apply(lambda x: x - 1)
+
+		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}")