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import datasets |
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import pandas as pd |
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import csv |
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import os |
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_ORIGIN = "http://dataome.mensxmachina.org/" |
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_CITATION = """ """ |
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class BioDataome(datasets.GeneratorBasedBuilder): |
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METADATA = pd.read_csv(f"http://dataome.mensxmachina.org/biodataome_data.csv") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name=i, |
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version=datasets.Version("1.0.0"), |
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description=d) |
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for i, d in zip( |
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METADATA["GSE"], |
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METADATA["Disease"], |
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) |
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] |
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def _info(self) -> datasets.DatasetInfo: |
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return datasets.DatasetInfo( |
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description="", |
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citation=_CITATION, |
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homepage=_ORIGIN, |
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license="", |
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) |
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def _split_generators(self, dl_manager): |
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gse = self.config.name |
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url = self.METADATA[self.METADATA["GSE"] == gse]["Datapath"].values[0] |
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metadata_url = self.METADATA[self.METADATA["GSE"] == gse]["DataAnnot"].values[0] |
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data: datasets.download.DownloadManager = dl_manager.download(url) |
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metadata: datasets.download.DownloadManager = dl_manager.download(metadata_url) |
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new_name = os.path.dirname(data) + "/" + os.path.basename(data).split(".")[0] + "_processed.csv" |
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df = pd.read_csv(data, index_col=0) |
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df = df.T |
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df.to_csv(new_name, index=False) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": new_name, "metadata": metadata}), |
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] |
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def _generate_examples(self, filepath, metadata): |
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print(filepath) |
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with open(filepath, "r") as f: |
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f_header = f.readline() |
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with open(metadata, "r") as m: |
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m_header = m.readline() |
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for key, (row, meta) in enumerate(zip(f, m)): |
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metadata = csv.reader([meta], quotechar='"').__next__() |
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row = row.split(",") |
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yield key, { |
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"data": |
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{ |
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i.strip(): j for i, j in zip(f_header.split(","), row) |
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}, |
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"metadata": |
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{ |
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i.strip(): j for i, j in zip(m_header.split(","), metadata) |
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} |
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} |
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if __name__ == "__main__": |
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ds = datasets.load_dataset("./load_script.py", "GSE17933") |
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ds['train'][0] |
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