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README.md

@@ -1,3 +1,21 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- golf
+- tabular_classification
+- binary_classification
+pretty_name: Golf
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- golf
 ---
+# Golf
+The Golf dataset.
+Is it a good day to play golf?
+
+# Configurations and tasks
+| **Configuration**     | **Task**                  |
+|-----------------------|---------------------------|
+| golf                  | Binary classification.|

golf.data

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+outlook,temperature,humidity,windy,goodPlaying,toPlay
+sunny,85,85,false,1,Don't Play
+sunny,80,90,true,1,Don't Play
+overcast,83,78,false,1.5,Play
+rain,70,96,false,0.8,Play
+rain,68,80,false,2,Play
+rain,65,70,true,1,Don't Play
+overcast,64,65,true,2.5,Play
+sunny,72,95,false,1,Don't Play
+sunny,69,70,false,1,Play
+rain,75,80,false,1.5,Play
+sunny,75,70,true,3,Play
+overcast,72,90,true,1.5,Play
+overcast,81,75,false,1,Play
+rain,71,80,true,1,Don't Play

golf.py

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+"""Golf Dataset"""
+
+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+_ENCODING_DICS = {
+	"toPlay": {
+		"Don't Play": 0,
+		"Play": 1
+	},
+	"windy": {
+		"false": False,
+		"true": True
+	}
+}
+
+DESCRIPTION = "Golf dataset."
+_HOMEPAGE = ""
+_URLS = ("")
+_CITATION = """"""
+
+# Dataset info
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/golf/resolve/main/golf.data"
+}
+features_types_per_config = {
+	"golf": {
+		"outlook": datasets.Value("string"),
+		"temperature": datasets.Value("int8"),
+		"humidity": datasets.Value("int8"),
+		"windy": datasets.Value("bool"),
+		"goodPlaying": datasets.Value("float64"),
+		"toPlay": datasets.ClassLabel(num_classes=2, names=("no", "yes"))
+	}
+}
+features_types_per_config["golf"]["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 GolfConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(GolfConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Golf(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "golf"
+	BUILDER_CONFIGS = [
+		GolfConfig(name="golf", description="Golf 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:
+		for feature in _ENCODING_DICS:
+			encoding_function = partial(self.encode, feature)
+			data.loc[:, 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}")