Upload hayes_roth.py

hayes_roth.py

@@ -24,43 +24,43 @@ _CITATION = """
 # Dataset info
 _BASE_FEATURE_NAMES = [
 	"name",
-    "hobby",
-    "age",
-    "educational_level",
-    "marital_level",
-    "class"
+	"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)
+		"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_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_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)
+	"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}
@@ -107,13 +107,13 @@ class Hayes(datasets.GeneratorBasedBuilder):
 	
 	def preprocess(self, data: pandas.DataFrame) -> pandas.DataFrame:
 		data.columns = _BASE_FEATURE_NAMES
-        data.drop("name", axis="columns", inplace=True)
+		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":
+		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":
+		elif self.config.name == "hayes_3":
 			data.loc[:, "class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
 
 		return data