Upload car.py

car.py

@@ -24,30 +24,30 @@ _BASE_FEATURE_NAMES = [
 	"class"
 ]
 urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/car/raw/main/car.data"
+	"train": "https://huggingface.co/datasets/mstz/car/raw/main/car.data"
 }
 features_types_per_config = {
-    "car": {
-        "buying": datasets.Value("int8"),
+	"car": {
+		"buying": datasets.Value("int8"),
 		"maint": datasets.Value("int8"),
 		"doors": datasets.Value("int8"),
 		"persons": datasets.Value("int8"),
 		"lug_boot": datasets.Value("int8"),
 		"safety": datasets.Value("int8"),
-        "class": datasets.ClassLabel(num_classes=4,
+		"class": datasets.ClassLabel(num_classes=4,
 									 names=("unacceptable", "acceptable", "good", "very good"))
-    },
+	},
 	"car_binary": {
-        "buying": datasets.Value("int8"),
+		"buying": datasets.Value("int8"),
 		"maint": datasets.Value("int8"),
 		"doors": datasets.Value("int8"),
 		"persons": datasets.Value("int8"),
 		"lug_boot": datasets.Value("int8"),
 		"safety": datasets.Value("int8"),
-        "class": datasets.ClassLabel(num_classes=2,
+		"class": datasets.ClassLabel(num_classes=2,
 									 names=("unacceptable", "acceptable"))
-    },
-    
+	},
+	
 }
 features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
 
@@ -92,58 +92,58 @@ _ENCODING_DICS = {
 }
 
 class CarConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(CarConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
+	def __init__(self, **kwargs):
+		super(CarConfig, self).__init__(version=VERSION, **kwargs)
+		self.features = features_per_config[kwargs["name"]]
 
 
 class Car(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "car"
-    BUILDER_CONFIGS = [
-        CarConfig(name="car",
-                    description="Car for 4-ary classification."),
+	# dataset versions
+	DEFAULT_CONFIG = "car"
+	BUILDER_CONFIGS = [
+		CarConfig(name="car",
+					description="Car for 4-ary classification."),
 		CarConfig(name="car_binary",
-                    description="Car for binary classification."),
-        ]
+					description="Car for binary classification."),
+		]
 
 
-    def _info(self):       
-        info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE,
-                                    features=features_per_config[self.config.name])
+	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 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)
+		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)
+		for row_id, row in data.iterrows():
+			data_row = dict(row)
 
-            yield row_id, data_row
+			yield row_id, data_row
 	
 	def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
-        data.columns = _BASE_FEATURE_NAMES
+		data.columns = _BASE_FEATURE_NAMES
 
-        for feature in _ENCODING_DICS:
-            encoding_function = partial(self.encode, feature)
-            data.loc[:, feature] = data[feature].apply(encoding_function)
+		for feature in _ENCODING_DICS:
+			encoding_function = partial(self.encode, feature)
+			data.loc[:, feature] = data[feature].apply(encoding_function)
 
-        if config == "car_binary":
+		if config == "car_binary":
 			data.loc[:, "class"] = data["class"].apply(lambda x: if x == 0 else 1)
 
 
 		return data
-    
-    def encode(self, feature, value):
-        if feature in _ENCODING_DICS:
-            return _ENCODING_DICS[feature][value]
-        raise ValueError(f"Unknown feature: {feature}")
+	
+	def encode(self, feature, value):
+		if feature in _ENCODING_DICS:
+			return _ENCODING_DICS[feature][value]
+		raise ValueError(f"Unknown feature: {feature}")