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from pathlib import Path |
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import polars as pl |
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from sklearn.model_selection import train_test_split, GroupShuffleSplit |
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import argparse |
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parser = argparse.ArgumentParser(description="Create dataset") |
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parser.add_argument("dataset", type=Path, help="dataset file name") |
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args = parser.parse_args() |
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filepath = args.dataset |
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data = pl.read_csv( |
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filepath, |
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use_pyarrow=True, |
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columns=["INGRESO", "FECHANACIMIENTO", "SOSPECHA_DIAGNOSTICA", "GES"], |
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) |
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data = data.with_columns( |
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age=((data["INGRESO"] - data["FECHANACIMIENTO"]).dt.days() / 365.25).cast(pl.Int16) |
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) |
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data = data.filter(pl.col("age") > 0) |
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data = data.with_columns( |
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GES=data["GES"].cast(pl.Boolean).cast(str), |
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) |
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data = data.drop(["INGRESO", "FECHANACIMIENTO"]) |
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data = data.with_columns( |
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pl.struct(["age", "SOSPECHA_DIAGNOSTICA"]) |
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.apply(lambda x: f"Paciente de {x['age']} años. {x['SOSPECHA_DIAGNOSTICA']}") |
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.alias("text") |
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).drop(["age", "SOSPECHA_DIAGNOSTICA"]) |
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data = data.rename({"GES": "label"}) |
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train_interim_splitter = GroupShuffleSplit(n_splits=1, train_size=0.7, random_state=11) |
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train_indices, interim_indices = next( |
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train_interim_splitter.split(data["text"], data["label"], data["text"]) |
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) |
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train = data[train_indices] |
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interim = data[interim_indices] |
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test_validation_splitter = GroupShuffleSplit( |
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n_splits=1, train_size=2 / 3, random_state=11 |
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) |
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test_indices, validation_indices = next( |
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test_validation_splitter.split(interim["text"], interim["label"], interim["text"]) |
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) |
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test = interim[test_indices] |
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validation = interim[validation_indices] |
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train.write_parquet("train.parquet") |
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test.write_parquet("test.parquet") |
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validation.write_parquet("validation.parquet") |
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