From c8c33c1c756f7e68796029d452480a58862a66f8 Mon Sep 17 00:00:00 2001 From: RYDE-WORK Date: Sat, 28 Feb 2026 16:48:45 +0800 Subject: [PATCH 1/2] =?UTF-8?q?=E6=92=A4=E5=9B=9E=E6=AD=A3=E5=88=99?= =?UTF-8?q?=E5=8C=96=E6=9B=B4=E6=94=B9?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- data/interim/internal_corrected.csv | 439 ---------------------------- lnp_ml/modeling/benchmark.py | 14 +- 2 files changed, 7 insertions(+), 446 deletions(-) delete mode 100644 data/interim/internal_corrected.csv diff --git a/data/interim/internal_corrected.csv b/data/interim/internal_corrected.csv deleted file mode 100644 index 19987bf..0000000 --- a/data/interim/internal_corrected.csv +++ /dev/null @@ -1,439 +0,0 @@ -smiles,Helper_lipid_ID,Helper_lipid_ID_DOPE,Helper_lipid_ID_DOTAP,Helper_lipid_ID_DSPC,Helper_lipid_ID_MDOA,Cationic_Lipid_to_mRNA_weight_ratio,Cationic_Lipid_Mol_Ratio,Phospholipid_Mol_Ratio,Cholesterol_Mol_Ratio,PEG_Lipid_Mol_Ratio,Purity,Mix_type,Cargo_type,Cargo_type_mRNA,Cargo_type_pDNA,Cargo_type_siRNA,Target_or_delivered_gene,Model_type,Model_type_A549,Model_type_BDMC,Model_type_BMDM,Model_type_HBEC_ALI,Model_type_HEK293T,Model_type_HeLa,Model_type_IGROV1,Model_type_Mouse,Model_type_RAW264p7,Delivery_target,Delivery_target_body,Delivery_target_dendritic_cell,Delivery_target_generic_cell,Delivery_target_liver,Delivery_target_lung,Delivery_target_lung_epithelium,Delivery_target_macrophage,Delivery_target_muscle,Delivery_target_spleen,Route_of_administration,Route_of_administration_in_vitro,Route_of_administration_intramuscular,Route_of_administration_intratracheal,Route_of_administration_intravenous,Batch_or_individual_or_barcoded,Batch_or_individual_or_barcoded_Barcoded,Batch_or_individual_or_barcoded_Individual,Value_name,Formulation_ID,Cationic_Lipid_Mass_Ratio,Phospholipid_Mass_Ratio,Cholesterol_Mass_Ratio,PEG_Lipid_Mass_Ratio,Amine,Tail,Lipid_name,Library_ID,Experiment_ID,Experimenter_ID,Experiment_weight,Goal,screen_id,Source,Sample_weight,Comment,Amine_number,Full_SMILES,Amine_SMILES,Tail_length,Num_tails,Ketone,Isocyanide,Is_cyclic,Formulation,Batch_or_individual,Random_id,4CR_Lipid_name,Aldehyde,Carboxylic_acid,Amine_name,Tail_name,Linker,MolWt,Tail_count,Num tails,Common_name,Ester,Disulfide,Total_radiance,Form_comment,molecular_weight,split_name_for_normalization,Encapsulation_Efficiency_EE<50,Encapsulation_Efficiency_50<=EE<80,Encapsulation_Efficiency_80 Date: Sat, 28 Feb 2026 16:49:34 +0800 Subject: [PATCH 2/2] =?UTF-8?q?=E5=8A=A0=E8=BD=BD=E6=A8=A1=E5=9E=8B?= =?UTF-8?q?=E6=97=B6=E6=98=BE=E5=BC=8Fweight=5Fonly=3DFalse?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lnp_ml/modeling/final_train_optuna_cv.py | 2 +- lnp_ml/modeling/nested_cv_optuna.py | 2 +- lnp_ml/modeling/predict.py | 2 +- lnp_ml/modeling/pretrain.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/lnp_ml/modeling/final_train_optuna_cv.py b/lnp_ml/modeling/final_train_optuna_cv.py index 9e36152..9e2173f 100644 --- a/lnp_ml/modeling/final_train_optuna_cv.py +++ b/lnp_ml/modeling/final_train_optuna_cv.py @@ -453,7 +453,7 @@ def main( if init_from_pretrain is not None: if init_from_pretrain.exists(): logger.info(f"Loading pretrain weights from {init_from_pretrain}") - checkpoint = torch.load(init_from_pretrain, map_location="cpu") + checkpoint = torch.load(init_from_pretrain, map_location="cpu", weights_only=False) pretrain_state_dict = checkpoint["model_state_dict"] pretrain_config = checkpoint.get("config", {}) logger.success(f"Loaded pretrain checkpoint (d_model={pretrain_config.get('d_model')})") diff --git a/lnp_ml/modeling/nested_cv_optuna.py b/lnp_ml/modeling/nested_cv_optuna.py index c2c24e4..8cc26d0 100644 --- a/lnp_ml/modeling/nested_cv_optuna.py +++ b/lnp_ml/modeling/nested_cv_optuna.py @@ -744,7 +744,7 @@ def main( if init_from_pretrain is not None: if init_from_pretrain.exists(): logger.info(f"Loading pretrain weights from {init_from_pretrain}") - checkpoint = torch.load(init_from_pretrain, map_location="cpu") + checkpoint = torch.load(init_from_pretrain, map_location="cpu", weights_only=False) pretrain_state_dict = checkpoint["model_state_dict"] pretrain_config = checkpoint.get("config", {}) logger.success(f"Loaded pretrain checkpoint (d_model={pretrain_config.get('d_model')})") diff --git a/lnp_ml/modeling/predict.py b/lnp_ml/modeling/predict.py index 88facd5..6722da8 100644 --- a/lnp_ml/modeling/predict.py +++ b/lnp_ml/modeling/predict.py @@ -38,7 +38,7 @@ def load_model( 自动根据 checkpoint 的 config.use_mpnn 选择模型类型。 """ - checkpoint = torch.load(model_path, map_location=device) + checkpoint = torch.load(model_path, map_location=device, weights_only=False) config = checkpoint["config"] use_mpnn = config.get("use_mpnn", False) diff --git a/lnp_ml/modeling/pretrain.py b/lnp_ml/modeling/pretrain.py index 9a23288..abb702d 100644 --- a/lnp_ml/modeling/pretrain.py +++ b/lnp_ml/modeling/pretrain.py @@ -392,7 +392,7 @@ def test( # 加载模型 logger.info(f"Loading pretrain model from {model_path}") - checkpoint = torch.load(model_path, map_location=device_obj) + checkpoint = torch.load(model_path, map_location=device_obj, weights_only=False) config = checkpoint["config"] # 解析 MPNN 配置