updated to datasets 4.*

README.md

@@ -1,21 +1,55 @@
 ---
-language:
-- en
+configs:
+- config_name: shuttle_0
+  data_files:
+  - path: shuttle_0/train.csv
+    split: train
+  default: true
+- config_name: shuttle_1
+  data_files:
+  - path: shuttle_1/train.csv
+    split: train
+  default: false
+- config_name: shuttle_2
+  data_files:
+  - path: shuttle_2/train.csv
+    split: train
+  default: false
+- config_name: shuttle_3
+  data_files:
+  - path: shuttle_3/train.csv
+    split: train
+  default: false
+- config_name: shuttle_4
+  data_files:
+  - path: shuttle_4/train.csv
+    split: train
+  default: false
+- config_name: shuttle_5
+  data_files:
+  - path: shuttle_5/train.csv
+    split: train
+  default: false
+- config_name: shuttle_6
+  data_files:
+  - path: shuttle_6/train.csv
+    split: train
+  default: false
+- config_name: shuttle
+  data_files:
+  - path: shuttle/train.csv
+    split: train
+  default: false
+language: en
+license: cc
+pretty_name: Shuttle
+size_categories: 1M<n<10M
 tags:
-- shuttle
 - tabular_classification
 - binary_classification
 - multiclass_classification
-- UCI
-pretty_name: Shuttle
-size_categories:
-- 10K<n<100K
 task_categories:
 - tabular-classification
-configs:
-- shuttle
-- shuttle_binary
-license: cc
 ---
 # Shuttle
 The [Shuttle dataset](https://archive-beta.ics.uci.edu/dataset/146/statlog+shuttle+satellite) from the [UCI repository](https://archive-beta.ics.uci.edu/).

shuttle.py

@@ -1,202 +0,0 @@
-"""Shuttle Dataset"""
-
-from typing import List
-from functools import partial
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-_ENCODING_DICS = {}
-
-DESCRIPTION = "Shuttle dataset."
-_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/148/statlog+shuttle"
-_URLS = ("https://archive-beta.ics.uci.edu/dataset/148/statlog+shuttle")
-_CITATION = """
-@misc{misc_statlog_(shuttle)_148,
-  title        = {{Statlog (Shuttle)}},
-  howpublished = {UCI Machine Learning Repository},
-  note         = {{DOI}: \\url{10.24432/C5WS31}}
-}
-"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/shuttle/raw/main/shuttle.csv"
-}
-features_types_per_config = {
-    "shuttle": {
-        "time": datasets.Value("float64"),
-        "rad_flow": datasets.Value("float64"),
-        "fpv_close": datasets.Value("float64"),
-        "fpv_open": datasets.Value("float64"),
-        "high": datasets.Value("float64"),
-        "bypass": datasets.Value("float64"),
-        "bvp_close": datasets.Value("float64"),
-        "bvp_open": datasets.Value("float64"),
-        "feature": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=7),
-    },
-    "shuttle_0": {
-        "time": datasets.Value("float64"),
-        "rad_flow": datasets.Value("float64"),
-        "fpv_close": datasets.Value("float64"),
-        "fpv_open": datasets.Value("float64"),
-        "high": datasets.Value("float64"),
-        "bypass": datasets.Value("float64"),
-        "bvp_close": datasets.Value("float64"),
-        "bvp_open": datasets.Value("float64"),
-        "feature": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2),
-    },
-    "shuttle_1": {
-        "time": datasets.Value("float64"),
-        "rad_flow": datasets.Value("float64"),
-        "fpv_close": datasets.Value("float64"),
-        "fpv_open": datasets.Value("float64"),
-        "high": datasets.Value("float64"),
-        "bypass": datasets.Value("float64"),
-        "bvp_close": datasets.Value("float64"),
-        "bvp_open": datasets.Value("float64"),
-        "feature": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2),
-    },
-    "shuttle_2": {
-        "time": datasets.Value("float64"),
-        "rad_flow": datasets.Value("float64"),
-        "fpv_close": datasets.Value("float64"),
-        "fpv_open": datasets.Value("float64"),
-        "high": datasets.Value("float64"),
-        "bypass": datasets.Value("float64"),
-        "bvp_close": datasets.Value("float64"),
-        "bvp_open": datasets.Value("float64"),
-        "feature": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2),
-    },
-    "shuttle_3": {
-        "time": datasets.Value("float64"),
-        "rad_flow": datasets.Value("float64"),
-        "fpv_close": datasets.Value("float64"),
-        "fpv_open": datasets.Value("float64"),
-        "high": datasets.Value("float64"),
-        "bypass": datasets.Value("float64"),
-        "bvp_close": datasets.Value("float64"),
-        "bvp_open": datasets.Value("float64"),
-        "feature": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2),
-    },
-    "shuttle_4": {
-        "time": datasets.Value("float64"),
-        "rad_flow": datasets.Value("float64"),
-        "fpv_close": datasets.Value("float64"),
-        "fpv_open": datasets.Value("float64"),
-        "high": datasets.Value("float64"),
-        "bypass": datasets.Value("float64"),
-        "bvp_close": datasets.Value("float64"),
-        "bvp_open": datasets.Value("float64"),
-        "feature": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2),
-    },
-    "shuttle_5": {
-        "time": datasets.Value("float64"),
-        "rad_flow": datasets.Value("float64"),
-        "fpv_close": datasets.Value("float64"),
-        "fpv_open": datasets.Value("float64"),
-        "high": datasets.Value("float64"),
-        "bypass": datasets.Value("float64"),
-        "bvp_close": datasets.Value("float64"),
-        "bvp_open": datasets.Value("float64"),
-        "feature": datasets.Value("float64"),
-        "class": datasets.ClassLabel(num_classes=2),
-    },
-    "shuttle_6": {
-        "time": datasets.Value("float64"),
-        "rad_flow": datasets.Value("float64"),
-        "fpv_close": datasets.Value("float64"),
-        "fpv_open": datasets.Value("float64"),
-        "high": datasets.Value("float64"),
-        "bypass": datasets.Value("float64"),
-        "bvp_close": datasets.Value("float64"),
-        "bvp_open": datasets.Value("float64"),
-        "feature": 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 ShuttleConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(ShuttleConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Shuttle(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "shuttle"
-    BUILDER_CONFIGS = [
-        ShuttleConfig(name="shuttle", description="Shuttle for multiclass classification."),
-        ShuttleConfig(name="shuttle_0", description="Shuttle for binary classification."),
-        ShuttleConfig(name="shuttle_1", description="Shuttle for binary classification."),
-        ShuttleConfig(name="shuttle_2", description="Shuttle for binary classification."),
-        ShuttleConfig(name="shuttle_3", description="Shuttle for binary classification."),
-        ShuttleConfig(name="shuttle_4", description="Shuttle for binary classification."),
-        ShuttleConfig(name="shuttle_5", description="Shuttle for binary classification."),
-        ShuttleConfig(name="shuttle_6", description="Shuttle 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:
-        data["class"] = data["class"].apply(lambda x: x - 1)
-
-        if self.config.name == "shuttle_0":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 0 else 0)
-        elif self.config.name == "shuttle_1":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 1 else 0)
-        elif self.config.name == "shuttle_2":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 2 else 0)
-        elif self.config.name == "shuttle_3":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 3 else 0)
-        elif self.config.name == "shuttle_4":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 4 else 0)
-        elif self.config.name == "shuttle_5":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 5 else 0)
-        elif self.config.name == "shuttle_6":
-            data["class"] = data["class"].apply(lambda x: 1 if x == 6 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}")