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README.md

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+---
+language:
+- en
+tags:
+- madelon
+- tabular_classification
+pretty_name: Annealing
+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:
+- Madelon
+---
+# 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).
+
+# Configurations and tasks
+- `madelon` for binary classification.

madelon.py

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+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("float32") for i in range(500)}
+}
+
+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.TEST, gen_kwargs={"filepath": downloads["test"]})
+        ]
+    
+    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:
+        features_types_per_config["madelon"].update({"500": datasets.ClassLabel(num_classes=2)})
+        data.columns = list(range(501))
+        data.loc[:, "500"] = data["500"].apply(lambda x: x if x > 0 else 0)
+
+        return data