updated to datasets 4.*

README.md

@@ -1,18 +1,20 @@
 ---
-language:
-- en
+configs:
+- config_name: madelon
+  data_files:
+  - path: madelon/train.csv
+    split: train
+  default: true
+language: en
+license: cc
+pretty_name: Madelon
+size_categories: 1M<n<10M
 tags:
-- madelon
 - tabular_classification
-- UCI
-pretty_name: Madelon
-size_categories:
-- 1K<n<10K
+- binary_classification
+- multiclass_classification
 task_categories:
 - tabular-classification
-configs:
-- Madelon
-license: cc
 ---
 # Annealing
 The [Madelon dataset](https://archive-beta.ics.uci.edu/dataset/171/madelon) from the [UCI ML repository](https://archive.ics.uci.edu/ml/datasets).

madelon.py

@@ -1,75 +0,0 @@
-from typing import List
-
-import datasets
-
-import pandas
-
-
-VERSION = datasets.Version("1.0.0")
-
-
-DESCRIPTION = "Madelon dataset from the UCI ML repository."
-_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/3/madelon"
-_URLS = ("https://archive-beta.ics.uci.edu/dataset/3/madelon")
-_CITATION = """"""
-
-# Dataset info
-urls_per_split = {
-    "train": "https://huggingface.co/datasets/mstz/madelon/raw/main/madelon_train.csv",
-    "validation": "https://huggingface.co/datasets/mstz/madelon/raw/main/madelon_valid.csv"
-}
-features_types_per_config = {
-    "madelon": {str(i): datasets.Value("int16") for i in range(500)}
-}
-features_types_per_config["madelon"]["500"] = datasets.ClassLabel(num_classes=2)
-
-features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config}
-
-
-class MadelonConfig(datasets.BuilderConfig):
-    def __init__(self, **kwargs):
-        super(MadelonConfig, self).__init__(version=VERSION, **kwargs)
-        self.features = features_per_config[kwargs["name"]]
-
-
-class Madelon(datasets.GeneratorBasedBuilder):
-    # dataset versions
-    DEFAULT_CONFIG = "madelon"
-    BUILDER_CONFIGS = [
-        MadelonConfig(name="madelon",
-                    description="Madelon for multiclass classification.")
-    ]
-
-
-    def _info(self):
-        if self.config.name not in features_per_config:
-            raise ValueError(f"Unknown configuration: {self.config.name}")
-        
-        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"]}),
-            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloads["validation"]})
-        ]
-    
-    def _generate_examples(self, filepath: str):
-            data = pandas.read_csv(filepath)
-            data = self.preprocess(data, config=self.config.name)
-
-            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["500"] = data["500"].apply(lambda x: max(0, x)).astype(int)
-        if "0.1" in data:
-            data.drop("0.1", axis="columns", inplace=True)
-
-        return data