Datasets:

mstz
/

car

 ---
+language:
+- en
+tags:
+- car
+- tabular_classification
+- binary_classification
+pretty_name: Car evaluation
+size_categories:
+- 1k<n<10K
+task_categories:  # Full list at https://github.com/huggingface/hub-docs/blob/main/js/src/lib/interfaces/Types.ts
+- tabular-classification
+configs:
+- car
 ---
+# Car
+The [Car dataset](https://archive-beta.ics.uci.edu/dataset/19/car+evaluation) from the [UCI repository](https://archive-beta.ics.uci.edu).
+# Configurations and tasks
+| **Configuration** | **Task**                  | **Description**         |
+|-------------------|---------------------------|-------------------------|
+| car               | Multiclass classification | What is the acceptability level of the car?|
+| car_binary        | Binary classification     | Is the car acceptable?|
+# Usage
+```python
+from datasets import load_dataset
+dataset = load_dataset("mstz/car", "car_binary")["train"]
+```

car.data ADDED Viewed

	@@ -0,0 +1,1728 @@

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car.py ADDED Viewed

	@@ -0,0 +1,149 @@

+from typing import List
+import datasets
+import pandas
+VERSION = datasets.Version("1.0.0")
+DESCRIPTION = "Car dataset from the UCI repository."
+_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/19/car+evaluation"
+_URLS = ("https://archive-beta.ics.uci.edu/dataset/19/car+evaluation")
+_CITATION = """"""
+# Dataset info
+_BASE_FEATURE_NAMES = [
+	"buying",
+	"maint",
+	"doors",
+	"persons",
+	"lug_boot",
+	"safety",
+	"class"
+]
+urls_per_split = {
+    "train": "https://huggingface.co/datasets/mstz/car/raw/main/car.data"
+}
+features_types_per_config = {
+    "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,
+									 names=("unacceptable", "acceptable", "good", "very good"))
+    },
+	"car_binary": {
+        "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,
+									 names=("unacceptable", "acceptable"))
+    },
+}
+features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
+_ENCODING_DICS = {
+	"buying": {
+		"vhigh": 3,
+		"high": 2,
+		"med": 1,
+		"low": 0
+	},
+	"maint": {
+		"vhigh": 3,
+		"high": 2,
+		"med": 1,
+		"low": 0
+	},
+	"persons": {
+		"0": 0,
+		"1": 1,
+		"2": 2,
+		"3": 3,
+		"4": 4,
+		"more": 5
+	},
+	"lug_boot": {
+		"big": 2,
+		"med": 1,
+		"small": 0,
+	},
+	"safety": {
+		"high": 2,
+		"med": 1,
+		"low": 0,
+	},
+	"acceptability_level": {
+		"unacc": 0,
+		"acc ": 1,
+		"good": 2,
+		"v-good": 3
+	}
+}
+class CarConfig(datasets.BuilderConfig):
+    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."),
+		CarConfig(name="car_binary",
+                    description="Car 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, config: str = DEFAULT_CONFIG) -> pandas.DataFrame:
+        data.columns = _BASE_FEATURE_NAMES
+        for feature in _ENCODING_DICS:
+            encoding_function = partial(self.encode, feature)
+            data.loc[:, feature] = data[feature].apply(encoding_function)
+        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}")