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from typing import List |
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import datasets |
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import pandas |
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VERSION = datasets.Version("1.0.0") |
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DESCRIPTION = "Madelon dataset from the UCI ML repository." |
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_HOMEPAGE = "https://archive-beta.ics.uci.edu/dataset/3/madelon" |
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_URLS = ("https://archive-beta.ics.uci.edu/dataset/3/madelon") |
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_CITATION = """""" |
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urls_per_split = { |
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"train": "https://huggingface.co/datasets/mstz/madelon/raw/main/madelon_train.csv", |
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"validation": "https://huggingface.co/datasets/mstz/madelon/raw/main/madelon_valid.csv" |
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} |
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features_types_per_config = { |
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"madelon": {str(i): datasets.Value("float32") for i in range(501)} |
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} |
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features_types_per_config["madelon"]["500"] = datasets.ClassLabel(num_classes=2) |
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features_per_config = {k: datasets.Features(features_types_per_config[k]) for k in features_types_per_config} |
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class MadelonConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(MadelonConfig, self).__init__(version=VERSION, **kwargs) |
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self.features = features_per_config[kwargs["name"]] |
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class Madelon(datasets.GeneratorBasedBuilder): |
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DEFAULT_CONFIG = "madelon" |
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BUILDER_CONFIGS = [ |
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MadelonConfig(name="madelon", |
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description="Madelon for multiclass classification.") |
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] |
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def _info(self): |
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if self.config.name not in features_per_config: |
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raise ValueError(f"Unknown configuration: {self.config.name}") |
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info = datasets.DatasetInfo(description=DESCRIPTION, citation=_CITATION, homepage=_HOMEPAGE, |
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features=features_per_config[self.config.name]) |
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return info |
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def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]: |
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downloads = dl_manager.download_and_extract(urls_per_split) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloads["train"]}), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloads["validation"]}) |
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] |
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def _generate_examples(self, filepath: str): |
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data = pandas.read_csv(filepath) |
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print(f"loading {filepath}") |
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data = self.preprocess(data, config=self.config.name) |
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print("\n\n\npreprocessed\n\n\n") |
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print(data.head()) |
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print(data.columns) |
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print(data["500"]) |
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print("500" in features_types_per_config) |
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for row_id, row in data.iterrows(): |
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data_row = dict(row) |
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yield row_id, data_row |
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def preprocess(self, data: pandas.DataFrame, config: str = DEFAULT_CONFIG) -> pandas.DataFrame: |
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print("data.columns") |
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print(data.columns) |
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print("'500' in data.columns") |
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print("500" in data.columns) |
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print(data["499"]) |
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print(data["500"]) |
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data["500"].apply(lambda x: max(0, x)) |
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data.loc[:, "500"] |
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data.loc[:, "500"] = data["500"].apply(lambda x: max(0, x)) |
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return data |
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