mirror of
https://github.com/RYDE-WORK/lnp_ml.git
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86 lines
3.6 KiB
Python
86 lines
3.6 KiB
Python
"""数据清洗脚本：修正原始数据中的问题"""
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from pathlib import Path
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import numpy as np
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import pandas as pd
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import typer
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from loguru import logger
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from lnp_ml.config import RAW_DATA_DIR, INTERIM_DATA_DIR
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app = typer.Typer()
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@app.command()
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def main(
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input_path: Path = RAW_DATA_DIR / "internal_deleted_uncorrected.xlsx",
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output_path: Path = INTERIM_DATA_DIR / "internal_corrected.csv",
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):
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"""
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清洗原始数据，修正已知问题。
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修正内容：
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1. 修正肌肉注射组 Biodistribution_muscle=0.7745 的数据
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2. 修复阳性对照组 (Amine="Crtl") 的数据
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3. 按给药途径分组进行 z-score 标准化
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4. 对 size 列取 log
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"""
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logger.info(f"Loading data from {input_path}")
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df = pd.read_excel(input_path, header=2)
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logger.info(f"Loaded {len(df)} samples")
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# 修正肌肉注射组 0.7745 的数据
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logger.info("Correcting Biodistribution_muscle=0.7745 rows...")
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rows_to_correct = df[df["Biodistribution_muscle"] == 0.7745]
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for index, row in rows_to_correct.iterrows():
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total_biodistribution = pd.to_numeric(row[[
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"Biodistribution_lymph_nodes",
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"Biodistribution_heart",
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"Biodistribution_liver",
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"Biodistribution_spleen",
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"Biodistribution_lung",
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"Biodistribution_kidney",
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"Biodistribution_muscle"
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]]).sum()
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df.at[index, "Biodistribution_lymph_nodes"] = pd.to_numeric(row["Biodistribution_lymph_nodes"]) / total_biodistribution
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df.at[index, "Biodistribution_heart"] = pd.to_numeric(row["Biodistribution_heart"]) / total_biodistribution
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df.at[index, "Biodistribution_liver"] = pd.to_numeric(row["Biodistribution_liver"]) / total_biodistribution
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df.at[index, "Biodistribution_spleen"] = pd.to_numeric(row["Biodistribution_spleen"]) / total_biodistribution
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df.at[index, "Biodistribution_lung"] = pd.to_numeric(row["Biodistribution_lung"]) / total_biodistribution
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df.at[index, "Biodistribution_kidney"] = pd.to_numeric(row["Biodistribution_kidney"]) / total_biodistribution
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df.at[index, "Biodistribution_muscle"] = pd.to_numeric(row["Biodistribution_muscle"]) / total_biodistribution
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df.at[index, "quantified_total_luminescence"] = pd.to_numeric(row["quantified_total_luminescence"]) / (1 - 0.7745)
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df.at[index, "unnormalized_delivery"] = df.at[index, "quantified_total_luminescence"]
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logger.info(f" Corrected {len(rows_to_correct)} rows")
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# 修复阳性对照组的数据
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logger.info("Fixing control group (Amine='Crtl')...")
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rows_to_override = df["Amine"] == "Crtl"
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df.loc[rows_to_override, "quantified_total_luminescence"] = 1
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df.loc[rows_to_override, "unnormalized_delivery"] = 1
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logger.info(f" Fixed {rows_to_override.sum()} rows")
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# 分别对肌肉注射组和静脉注射组重新进行 z-score 标准化
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logger.info("Z-score normalizing delivery by Route_of_administration...")
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df["unnormalized_delivery"] = pd.to_numeric(df["unnormalized_delivery"], errors="coerce")
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df["quantified_delivery"] = (
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df.groupby("Route_of_administration")["unnormalized_delivery"]
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.transform(lambda x: (x - x.mean()) / x.std())
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)
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# 对 size 列取 log
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logger.info("Log-transforming size column...")
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df["size"] = pd.to_numeric(df["size"], errors="coerce")
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df["size"] = np.log(df["size"].replace(0, np.nan)) # 避免 log(0)
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# 保存
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output_path.parent.mkdir(parents=True, exist_ok=True)
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df.to_csv(output_path, index=False)
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logger.success(f"Saved cleaned data to {output_path}")
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if __name__ == "__main__":
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app()
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