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README.md

@@ -1,3 +1,39 @@
 ---
-license: cc-by-4.0
+language:
+- en
+tags:
+- hayes
+- tabular_classification
+- binary_classification
+- multiclass_classification
+pretty_name: Hayes evaluation
+size_categories:
+- n<1k
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- hayes
+- hayes_1
+- hayes_2
+- hayes_3
+
 ---
+# Hayes
+The [Hayes-Roth dataset](https://archive-beta.ics.uci.edu/dataset/44/hayes+roth) from the [UCI repository](https://archive-beta.ics.uci.edu).
+
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**                |
+|-------------------|---------------------------|--------------------------------|
+| hayes             | Multiclass classification | Classify hayes type.           |
+| hayes_1           | Binary classification     | Is this instance of class 1?   |
+| hayes_2           | Binary classification     | Is this instance of class 2?   |
+| hayes_3           | Binary classification     | Is this instance of class 3?   |
+
+
+
+# Usage
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("mstz/hayes", "hayes")["train"]
+```

hayes_roth.data

@@ -0,0 +1,132 @@
+92,2,1,1,2,1
+10,2,1,3,2,2
+83,3,1,4,1,3
+61,2,4,2,2,3
+107,1,1,3,4,3
+113,1,1,3,2,2
+80,3,1,3,2,2
+125,3,4,2,4,3
+36,2,2,1,1,1
+105,3,2,1,1,1
+81,1,2,1,1,1
+122,2,2,3,4,3
+94,1,1,2,1,1
+60,2,1,2,2,2
+8,2,4,1,4,3
+20,1,1,3,3,1
+85,3,2,1,2,2
+50,1,2,1,1,1
+68,3,3,2,1,1
+89,3,1,3,2,1
+52,1,2,2,1,2
+19,3,2,1,3,1
+118,2,1,2,1,1
+16,3,2,1,3,1
+91,2,3,2,1,1
+79,3,2,2,1,2
+23,3,2,1,3,2
+25,2,1,2,2,2
+30,1,1,3,2,1
+57,3,2,1,1,1
+3,1,4,1,1,3
+114,2,2,1,3,1
+37,1,2,1,3,2
+66,1,1,1,2,1
+110,2,4,3,1,3
+116,3,1,2,2,2
+88,1,1,2,2,2
+77,3,2,2,1,2
+82,1,2,1,2,2
+64,3,4,3,2,3
+84,2,2,2,1,2
+86,2,2,1,2,2
+6,3,2,1,3,2
+74,3,2,1,1,1
+106,3,1,2,1,1
+115,1,2,1,3,2
+130,2,1,1,2,1
+54,1,1,1,2,1
+33,1,2,2,3,2
+67,3,3,1,1,1
+69,3,3,3,1,1
+39,3,2,1,2,2
+53,3,2,1,2,2
+127,3,1,2,1,1
+96,1,1,1,2,1
+121,2,1,3,2,1
+70,2,2,2,1,2
+123,2,1,2,1,1
+42,2,2,1,3,1
+78,2,1,2,2,2
+11,1,2,4,2,3
+129,2,2,1,2,2
+128,1,1,2,4,3
+5,1,3,2,1,1
+4,2,4,4,2,3
+95,2,3,2,1,1
+73,3,1,2,2,2
+26,1,1,2,2,2
+48,1,3,2,4,3
+104,1,1,2,2,2
+102,3,1,4,2,3
+2,2,1,3,2,2
+41,1,1,3,2,2
+119,3,1,3,2,1
+75,1,2,4,4,3
+47,1,4,2,1,3
+93,2,1,2,1,1
+46,3,4,1,2,3
+132,2,2,1,1,1
+108,1,1,2,1,1
+18,2,2,4,3,3
+62,3,1,2,2,2
+120,1,1,3,2,1
+35,1,2,1,3,1
+27,1,4,4,1,3
+98,3,3,3,2,2
+109,2,2,1,3,2
+31,3,3,2,1,2
+112,1,1,1,3,1
+34,2,2,1,2,2
+63,2,2,2,1,2
+65,2,3,2,3,2
+117,1,3,2,1,2
+56,2,2,1,2,2
+59,1,1,1,2,1
+76,3,2,2,1,2
+1,3,2,1,1,1
+28,1,1,2,1,1
+22,3,1,4,4,3
+29,3,3,2,1,2
+111,2,3,2,1,2
+97,2,1,3,1,1
+49,1,2,1,2,2
+51,3,1,1,2,1
+87,2,2,4,1,3
+58,1,2,2,1,2
+32,2,3,2,1,2
+72,2,2,1,4,3
+55,1,4,2,3,3
+103,2,2,1,1,1
+7,1,2,1,1,1
+99,2,2,3,2,2
+15,1,3,2,1,1
+126,3,1,2,1,1
+45,3,1,1,2,1
+101,3,3,1,4,3
+100,2,3,4,1,3
+24,1,2,3,3,2
+124,3,3,2,2,2
+13,3,3,4,2,3
+14,1,2,2,1,2
+38,2,1,1,4,3
+71,3,1,2,2,2
+43,3,2,2,4,3
+131,2,3,1,3,1
+17,2,1,1,2,1
+12,3,4,1,3,3
+44,1,1,4,3,3
+40,2,1,2,1,1
+90,1,2,1,2,2
+21,1,2,2,1,2
+9,3,1,1,2,1

hayes_roth.py

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+from typing import List
+from functools import partial
+
+import datasets
+
+import pandas
+
+
+VERSION = datasets.Version("1.0.0")
+
+
+DESCRIPTION = "Hayes efficiency dataset from the UCI repository."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/242/hayes+efficiency"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/30/hayes+method+choice")
+_CITATION = """
+@misc{misc_hayes_efficiency_242,
+  author       = {Tsanas,Athanasios & Xifara,Angeliki},
+  title        = {{Hayes efficiency}},
+  year         = {2012},
+  howpublished = {UCI Machine Learning Repository},
+  note         = {{DOI}: \\url{10.24432/C51307}}
+}"""
+
+# Dataset info
+_BASE_FEATURE_NAMES = [
+	"name",
+    "hobby",
+    "age",
+    "educational_level",
+    "marital_level",
+    "class"
+]
+urls_per_split = {
+	"train": "https://huggingface.co/datasets/mstz/hayes/raw/main/hayes_roth.data"
+}
+features_types_per_config = {
+	"hayes": {
+        "hobby": datasets.Value("string"),
+        "age": datasets.Value("int8"),
+        "educational_level": datasets.Value("int8"),
+        "marital_level": datasets.Value("string"),
+        "class": datasets.ClassLabel(num_classes=3)
+	},
+    "hayes_1": {
+        "hobby": datasets.Value("string"),
+        "age": datasets.Value("int8"),
+        "educational_level": datasets.Value("int8"),
+        "marital_level": datasets.Value("string"),
+        "class": datasets.ClassLabel(num_classes=2)
+	},
+    "hayes_2": {
+        "hobby": datasets.Value("string"),
+        "age": datasets.Value("int8"),
+        "educational_level": datasets.Value("int8"),
+        "marital_level": datasets.Value("string"),
+        "class": datasets.ClassLabel(num_classes=2)
+	},
+    "hayes_3": {
+        "hobby": datasets.Value("string"),
+        "age": datasets.Value("int8"),
+        "educational_level": datasets.Value("int8"),
+        "marital_level": datasets.Value("string"),
+        "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 HayesConfig(datasets.BuilderConfig):
+	def __init__(self, **kwargs):
+		super(HayesConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
+
+
+class Hayes(datasets.GeneratorBasedBuilder):
+	# dataset versions
+	DEFAULT_CONFIG = "hayes"
+	BUILDER_CONFIGS = [
+		HayesConfig(name="hayes", description="Hayes dataset."),
+		HayesConfig(name="hayes_1", description="Hayes for binary classification (is example of class 1?)."),
+		HayesConfig(name="hayes_2", description="Hayes for binary classification (is example of class 2?)."),
+		HayesConfig(name="hayes_3", description="Hayes for binary classification (is example of class 3?).")
+	]
+
+
+	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.drop("name", axis="columns", inplace=True)
+
+		if self.config.name == "hayes_1":
+			data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
+        elif self.config.name == "hayes_2":
+			data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
+        elif self.config.name == "hayes_3":
+			data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
+
+		return data