updated to datasets 4.*

README.md

@@ -1,20 +1,40 @@
 ---
-language:
-- en
+configs:
+- config_name: wall_following_0
+  data_files:
+  - path: wall_following_0/train.csv
+    split: train
+  default: true
+- config_name: wall_following_1
+  data_files:
+  - path: wall_following_1/train.csv
+    split: train
+  default: false
+- config_name: wall_following_2
+  data_files:
+  - path: wall_following_2/train.csv
+    split: train
+  default: false
+- config_name: wall_following_3
+  data_files:
+  - path: wall_following_3/train.csv
+    split: train
+  default: false
+- config_name: wall_following
+  data_files:
+  - path: wall_following/train.csv
+    split: train
+  default: false
+language: en
+license: cc
+pretty_name: Wall following
+size_categories: 1M<n<10M
 tags:
-- wall_following
 - tabular_classification
 - binary_classification
 - multiclass_classification
-- UCI
-pretty_name: WallFollowing
-size_categories:
-- 1K<n<5K
 task_categories:
 - tabular-classification
-configs:
-- wall_following
-license: cc
 ---
 # WallFollowing
 The [WallFollowing dataset](https://archive-beta.ics.uci.edu/dataset/194/wall+following+robot+navigation+data) from the [UCI repository](https://archive-beta.ics.uci.edu/).

wall_following.py

@@ -1,232 +0,0 @@
-"""WallFollowing Dataset"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-_ENCODING_DICS = {}
-
-DESCRIPTION = "WallFollowing dataset."
-_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/194/wall+following+robot+navigation+data"
-_URLS = ("https://archive-beta.ics.uci.edu/dataset/194/wall+following+robot+navigation+data")
-_CITATION = """
-@misc{misc_wall-following_robot_navigation_data_194,
-  author       = {Freire,Ananda, Veloso,Marcus & Barreto,Guilherme},
-  title        = {{Wall-Following Robot Navigation Data}},
-  year         = {2010},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C57C8W}}
-}
-"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/wall_following/raw/main/wall_following.csv"
-}
-features_types_per_config = {
-    "wall_following": {
-        "US1": datasets.Value("float64"),
-		"US2": datasets.Value("float64"),
-		"US3": datasets.Value("float64"),
-		"US4": datasets.Value("float64"),
-		"US5": datasets.Value("float64"),
-		"US6": datasets.Value("float64"),
-		"US7": datasets.Value("float64"),
-		"US8": datasets.Value("float64"),
-		"US9": datasets.Value("float64"),
-		"US10": datasets.Value("float64"),
-		"US11": datasets.Value("float64"),
-		"US12": datasets.Value("float64"),
-		"US13": datasets.Value("float64"),
-		"US14": datasets.Value("float64"),
-		"US15": datasets.Value("float64"),
-		"US16": datasets.Value("float64"),
-		"US17": datasets.Value("float64"),
-		"US18": datasets.Value("float64"),
-		"US19": datasets.Value("float64"),
-		"US20": datasets.Value("float64"),
-		"US21": datasets.Value("float64"),
-		"US22": datasets.Value("float64"),
-		"US23": datasets.Value("float64"),
-		"US24": datasets.Value("float64"),
-		"class": datasets.ClassLabel(num_classes=4),
-    },
-    "wall_following_0": {
-        "US1": datasets.Value("float64"),
-		"US2": datasets.Value("float64"),
-		"US3": datasets.Value("float64"),
-		"US4": datasets.Value("float64"),
-		"US5": datasets.Value("float64"),
-		"US6": datasets.Value("float64"),
-		"US7": datasets.Value("float64"),
-		"US8": datasets.Value("float64"),
-		"US9": datasets.Value("float64"),
-		"US10": datasets.Value("float64"),
-		"US11": datasets.Value("float64"),
-		"US12": datasets.Value("float64"),
-		"US13": datasets.Value("float64"),
-		"US14": datasets.Value("float64"),
-		"US15": datasets.Value("float64"),
-		"US16": datasets.Value("float64"),
-		"US17": datasets.Value("float64"),
-		"US18": datasets.Value("float64"),
-		"US19": datasets.Value("float64"),
-		"US20": datasets.Value("float64"),
-		"US21": datasets.Value("float64"),
-		"US22": datasets.Value("float64"),
-		"US23": datasets.Value("float64"),
-		"US24": datasets.Value("float64"),
-		"class": datasets.ClassLabel(num_classes=2),
-    },
-    "wall_following_1": {
-                "US1": datasets.Value("float64"),
-		"US2": datasets.Value("float64"),
-		"US3": datasets.Value("float64"),
-		"US4": datasets.Value("float64"),
-		"US5": datasets.Value("float64"),
-		"US6": datasets.Value("float64"),
-		"US7": datasets.Value("float64"),
-		"US8": datasets.Value("float64"),
-		"US9": datasets.Value("float64"),
-		"US10": datasets.Value("float64"),
-		"US11": datasets.Value("float64"),
-		"US12": datasets.Value("float64"),
-		"US13": datasets.Value("float64"),
-		"US14": datasets.Value("float64"),
-		"US15": datasets.Value("float64"),
-		"US16": datasets.Value("float64"),
-		"US17": datasets.Value("float64"),
-		"US18": datasets.Value("float64"),
-		"US19": datasets.Value("float64"),
-		"US20": datasets.Value("float64"),
-		"US21": datasets.Value("float64"),
-		"US22": datasets.Value("float64"),
-		"US23": datasets.Value("float64"),
-		"US24": datasets.Value("float64"),
-		"class": datasets.ClassLabel(num_classes=2),
-    },
-    "wall_following_2": {
-        "US1": datasets.Value("float64"),
-		"US2": datasets.Value("float64"),
-		"US3": datasets.Value("float64"),
-		"US4": datasets.Value("float64"),
-		"US5": datasets.Value("float64"),
-		"US6": datasets.Value("float64"),
-		"US7": datasets.Value("float64"),
-		"US8": datasets.Value("float64"),
-		"US9": datasets.Value("float64"),
-		"US10": datasets.Value("float64"),
-		"US11": datasets.Value("float64"),
-		"US12": datasets.Value("float64"),
-		"US13": datasets.Value("float64"),
-		"US14": datasets.Value("float64"),
-		"US15": datasets.Value("float64"),
-		"US16": datasets.Value("float64"),
-		"US17": datasets.Value("float64"),
-		"US18": datasets.Value("float64"),
-		"US19": datasets.Value("float64"),
-		"US20": datasets.Value("float64"),
-		"US21": datasets.Value("float64"),
-		"US22": datasets.Value("float64"),
-		"US23": datasets.Value("float64"),
-		"US24": datasets.Value("float64"),
-		"class": datasets.ClassLabel(num_classes=2),
-    },
-    "wall_following_3": {
-        "US1": datasets.Value("float64"),
-		"US2": datasets.Value("float64"),
-		"US3": datasets.Value("float64"),
-		"US4": datasets.Value("float64"),
-		"US5": datasets.Value("float64"),
-		"US6": datasets.Value("float64"),
-		"US7": datasets.Value("float64"),
-		"US8": datasets.Value("float64"),
-		"US9": datasets.Value("float64"),
-		"US10": datasets.Value("float64"),
-		"US11": datasets.Value("float64"),
-		"US12": datasets.Value("float64"),
-		"US13": datasets.Value("float64"),
-		"US14": datasets.Value("float64"),
-		"US15": datasets.Value("float64"),
-		"US16": datasets.Value("float64"),
-		"US17": datasets.Value("float64"),
-		"US18": datasets.Value("float64"),
-		"US19": datasets.Value("float64"),
-		"US20": datasets.Value("float64"),
-		"US21": datasets.Value("float64"),
-		"US22": datasets.Value("float64"),
-		"US23": datasets.Value("float64"),
-		"US24": datasets.Value("float64"),
-		"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 WallFollowingConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(WallFollowingConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class WallFollowing(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "wall_following"
-    BUILDER_CONFIGS = [
-        WallFollowingConfig(name="wall_following", description="WallFollowing for multiclass classification."),
-        WallFollowingConfig(name="wall_following_0", description="WallFollowing for binary classification."),
-        WallFollowingConfig(name="wall_following_1", description="WallFollowing for binary classification."),
-        WallFollowingConfig(name="wall_following_2", description="WallFollowing for binary classification."),
-        WallFollowingConfig(name="wall_following_3", description="WallFollowing 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)
-        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:
-        if self.config.name == "wall_following_0":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
-        elif self.config.name == "wall_following_1":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
-        elif self.config.name == "wall_following_2":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
-        elif self.config.name == "wall_following_3":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
-        
-        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}")