diff --git a/Makefile b/Makefile index 3835fb4..373e155 100644 --- a/Makefile +++ b/Makefile @@ -80,6 +80,11 @@ benchmark: requirements $(PYTHON_INTERPRETER) -m lnp_ml.modeling.benchmark main $(MPNN_FLAG) $(DEVICE_FLAG) $(PYTHON_INTERPRETER) -m lnp_ml.modeling.benchmark test $(DEVICE_FLAG) +## Evaluate baseline method on public test splits (test.csv vs preds.csv in cv_*) +.PHONY: baseline +baseline: requirements + $(PYTHON_INTERPRETER) scripts/evaluate_external.py + ################################################################################# # TRAINING (Nested CV + Optuna) # ################################################################################# diff --git a/lnp_ml/features.py b/lnp_ml/features.py deleted file mode 100644 index f103db7..0000000 --- a/lnp_ml/features.py +++ /dev/null @@ -1,29 +0,0 @@ -from pathlib import Path - -from loguru import logger -from tqdm import tqdm -import typer - -from lnp_ml.config import PROCESSED_DATA_DIR - -app = typer.Typer() - - -@app.command() -def main( - # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ---- - input_path: Path = PROCESSED_DATA_DIR / "dataset.csv", - output_path: Path = PROCESSED_DATA_DIR / "features.csv", - # ----------------------------------------- -): - # ---- REPLACE THIS WITH YOUR OWN CODE ---- - logger.info("Generating features from dataset...") - for i in tqdm(range(10), total=10): - if i == 5: - logger.info("Something happened for iteration 5.") - logger.success("Features generation complete.") - # ----------------------------------------- - - -if __name__ == "__main__": - app() diff --git a/lnp_ml/plots.py b/lnp_ml/plots.py deleted file mode 100644 index 34628ab..0000000 --- a/lnp_ml/plots.py +++ /dev/null @@ -1,29 +0,0 @@ -from pathlib import Path - -from loguru import logger -from tqdm import tqdm -import typer - -from lnp_ml.config import FIGURES_DIR, PROCESSED_DATA_DIR - -app = typer.Typer() - - -@app.command() -def main( - # ---- REPLACE DEFAULT PATHS AS APPROPRIATE ---- - input_path: Path = PROCESSED_DATA_DIR / "dataset.csv", - output_path: Path = FIGURES_DIR / "plot.png", - # ----------------------------------------- -): - # ---- REPLACE THIS WITH YOUR OWN CODE ---- - logger.info("Generating plot from data...") - for i in tqdm(range(10), total=10): - if i == 5: - logger.info("Something happened for iteration 5.") - logger.success("Plot generation complete.") - # ----------------------------------------- - - -if __name__ == "__main__": - app() diff --git a/scripts/data_cleaning.py b/scripts/data_cleaning.py deleted file mode 100644 index 8858626..0000000 --- a/scripts/data_cleaning.py +++ /dev/null @@ -1,85 +0,0 @@ -"""数据清洗脚本：修正原始数据中的问题""" - -from pathlib import Path - -import numpy as np -import pandas as pd -import typer -from loguru import logger - -from lnp_ml.config import RAW_DATA_DIR, INTERIM_DATA_DIR - - -app = typer.Typer() - - -@app.command() -def main( - input_path: Path = RAW_DATA_DIR / "internal_deleted_uncorrected.xlsx", - output_path: Path = INTERIM_DATA_DIR / "internal_corrected.csv", -): - """ - 清洗原始数据，修正已知问题。 - - 修正内容： - 1. 修正肌肉注射组 Biodistribution_muscle=0.7745 的数据 - 2. 修复阳性对照组 (Amine="Crtl") 的数据 - 3. 按给药途径分组进行 z-score 标准化 - 4. 对 size 列取 log - """ - logger.info(f"Loading data from {input_path}") - df = pd.read_excel(input_path, header=2) - logger.info(f"Loaded {len(df)} samples") - - # 修正肌肉注射组 0.7745 的数据 - logger.info("Correcting Biodistribution_muscle=0.7745 rows...") - rows_to_correct = df[df["Biodistribution_muscle"] == 0.7745] - for index, row in rows_to_correct.iterrows(): - total_biodistribution = pd.to_numeric(row[[ - "Biodistribution_lymph_nodes", - "Biodistribution_heart", - "Biodistribution_liver", - "Biodistribution_spleen", - "Biodistribution_lung", - "Biodistribution_kidney", - "Biodistribution_muscle" - ]]).sum() - df.at[index, "Biodistribution_lymph_nodes"] = pd.to_numeric(row["Biodistribution_lymph_nodes"]) / total_biodistribution - df.at[index, "Biodistribution_heart"] = pd.to_numeric(row["Biodistribution_heart"]) / total_biodistribution - df.at[index, "Biodistribution_liver"] = pd.to_numeric(row["Biodistribution_liver"]) / total_biodistribution - df.at[index, "Biodistribution_spleen"] = pd.to_numeric(row["Biodistribution_spleen"]) / total_biodistribution - df.at[index, "Biodistribution_lung"] = pd.to_numeric(row["Biodistribution_lung"]) / total_biodistribution - df.at[index, "Biodistribution_kidney"] = pd.to_numeric(row["Biodistribution_kidney"]) / total_biodistribution - df.at[index, "Biodistribution_muscle"] = pd.to_numeric(row["Biodistribution_muscle"]) / total_biodistribution - df.at[index, "quantified_total_luminescence"] = pd.to_numeric(row["quantified_total_luminescence"]) / (1 - 0.7745) - df.at[index, "unnormalized_delivery"] = df.at[index, "quantified_total_luminescence"] - logger.info(f" Corrected {len(rows_to_correct)} rows") - - # 修复阳性对照组的数据 - logger.info("Fixing control group (Amine='Crtl')...") - rows_to_override = df["Amine"] == "Crtl" - df.loc[rows_to_override, "quantified_total_luminescence"] = 1 - df.loc[rows_to_override, "unnormalized_delivery"] = 1 - logger.info(f" Fixed {rows_to_override.sum()} rows") - - # 分别对肌肉注射组和静脉注射组重新进行 z-score 标准化 - logger.info("Z-score normalizing delivery by Route_of_administration...") - df["unnormalized_delivery"] = pd.to_numeric(df["unnormalized_delivery"], errors="coerce") - df["quantified_delivery"] = ( - df.groupby("Route_of_administration")["unnormalized_delivery"] - .transform(lambda x: (x - x.mean()) / x.std()) - ) - - # 对 size 列取 log - logger.info("Log-transforming size column...") - df["size"] = pd.to_numeric(df["size"], errors="coerce") - df["size"] = np.log(df["size"].replace(0, np.nan)) # 避免 log(0) - - # 保存 - output_path.parent.mkdir(parents=True, exist_ok=True) - df.to_csv(output_path, index=False) - logger.success(f"Saved cleaned data to {output_path}") - - -if __name__ == "__main__": - app()