From cd9166d63c48ff2e0c1906c705017660ddae1fbb Mon Sep 17 00:00:00 2001 From: Michelle0574 <2170308303@qq.com> Date: Thu, 18 Jun 2026 08:40:19 +0000 Subject: [PATCH] =?UTF-8?q?feat(moe):=20MoE=20+=20MolT5=20LLM=20=E8=9E=8D?= =?UTF-8?q?=E5=90=88=E5=B1=82=E3=80=81=E6=B6=88=E8=9E=8D=E6=B5=81=E6=B0=B4?= =?UTF-8?q?=E7=BA=BF?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- lnp_ml/dataset.py | 1066 +-- lnp_ml/modeling/heads.py | 242 +- lnp_ml/modeling/layers/fusion.py | 263 +- lnp_ml/modeling/layers/llm_prompt.py | 104 + lnp_ml/modeling/layers/set_transformer.py | 123 + lnp_ml/modeling/models.py | 302 +- lnp_ml/modeling/nested_cv_optuna.py | 1954 ++--- lnp_ml/modeling/pretrain.py | 1095 +-- lnp_ml/modeling/trainer_balanced.py | 1074 +-- lnp_ml/utils/seed.py | 19 + .../outer_fold_0/best_params.json | 12 + .../20260617_180556/outer_fold_0/splits.json | 1 + .../outer_fold_0/test_metrics.json | 42 + .../outer_fold_1/best_params.json | 12 + 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"Cationic_Lipid_to_mRNA_weight_ratio", - "Cationic_Lipid_Mol_Ratio", - "Phospholipid_Mol_Ratio", - "Cholesterol_Mol_Ratio", - "PEG_Lipid_Mol_Ratio", -] - -# phys token: 物理/实验参数 one-hot [12] -# 需要从原始列生成 one-hot -PHYS_ONEHOT_SPECS = { - "Purity": ["Pure", "Crude"], - "Mix_type": ["Microfluidic", "Pipetting"], - "Cargo_type": ["mRNA", "pDNA", "siRNA"], - "Target_or_delivered_gene": ["FFL", "Peptide_barcode", "hEPO", "FVII", "GFP"], -} - -# help token: Helper lipid one-hot [4] -HELP_COLS = [ - "Helper_lipid_ID_DOPE", - "Helper_lipid_ID_DOTAP", - "Helper_lipid_ID_DSPC", - "Helper_lipid_ID_MDOA", -] - -# exp token: 实验条件 one-hot [32] -EXP_ONEHOT_SPECS = { - "Model_type": ["A549", "BDMC", "BMDM", "HBEC_ALI", "HEK293T", "HeLa", "IGROV1", "Mouse", "RAW264p7"], - "Delivery_target": ["body", "dendritic_cell", "generic_cell", "liver", "lung", "lung_epithelium", "macrophage", "muscle", "spleen"], - "Route_of_administration": ["in_vitro", "intramuscular", "intratracheal", "intravenous"], - "Batch_or_individual_or_barcoded": ["Barcoded", "Individual"], - "Value_name": ["log_luminescence", "luminescence", "FFL_silencing", "Peptide_abundance", "hEPO", "FVII_silencing", "GFP_delivery", "Discretized_luminescence"], -} - -# Target 列 -TARGET_REGRESSION = ["size", "quantified_delivery"] -TARGET_CLASSIFICATION_PDI = ["PDI_0_0to0_2", "PDI_0_2to0_3", "PDI_0_3to0_4", "PDI_0_4to0_5"] -TARGET_CLASSIFICATION_EE = ["Encapsulation_Efficiency_EE<50", "Encapsulation_Efficiency_50<=EE<80", "Encapsulation_Efficiency_80 List[str]: - """生成 one-hot 列名""" - return [f"{prefix}_{v}" for v in values] - - -def process_dataframe(df: pd.DataFrame) -> pd.DataFrame: - """ - 处理原始 DataFrame，生成模型所需的所有列。 - - Args: - df: 原始 DataFrame - - Returns: - 处理后的 DataFrame，包含所有需要的列 - """ - df = df.copy() - - # 1. 处理 phys token 的 one-hot 列（如果不存在则生成） - for col, values in PHYS_ONEHOT_SPECS.items(): - for v in values: - onehot_col = f"{col}_{v}" - if onehot_col not in df.columns: - if col in df.columns: - df[onehot_col] = (df[col] == v).astype(float) - else: - df[onehot_col] = 0.0 - - # 2. 处理 exp token 的 one-hot 列（如果不存在则生成） - for col, values in EXP_ONEHOT_SPECS.items(): - for v in values: - onehot_col = f"{col}_{v}" - if onehot_col not in df.columns: - if col in df.columns: - df[onehot_col] = (df[col] == v).astype(float) - else: - df[onehot_col] = 0.0 - - # 3. 确保 comp 列存在且为 float - for col in COMP_COLS: - if col in df.columns: - df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0.0) - else: - df[col] = 0.0 - - # 4. 确保 help 列存在 - for col in HELP_COLS: - if col not in df.columns: - df[col] = 0.0 - else: - df[col] = df[col].fillna(0.0).astype(float) - - # 5. 处理 target 列 - # size: 已经 log 过，填充缺失值 - if "size" in df.columns: - df["size"] = pd.to_numeric(df["size"], errors="coerce") - - # quantified_delivery: 已经 z-score 过 - if "quantified_delivery" in df.columns: - df["quantified_delivery"] = pd.to_numeric(df["quantified_delivery"], errors="coerce") - - # toxic: 0/1 - if TARGET_TOXIC in df.columns: - df[TARGET_TOXIC] = pd.to_numeric(df[TARGET_TOXIC], errors="coerce").fillna(-1).astype(int) - - # PDI 和 EE 的 one-hot 分类 - for col in TARGET_CLASSIFICATION_PDI + TARGET_CLASSIFICATION_EE: - if col in df.columns: - df[col] = df[col].fillna(0).astype(float) - - # Biodistribution - for col in TARGET_BIODIST: - if col in df.columns: - df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0.0) - - return df - - -def get_phys_cols() -> List[str]: - """获取 phys token 的所有列名""" - cols = [] - for col, values in PHYS_ONEHOT_SPECS.items(): - cols.extend(get_onehot_cols(col, values)) - return cols - - -def get_exp_cols() -> List[str]: - """获取 exp token 的所有列名""" - cols = [] - for col, values in EXP_ONEHOT_SPECS.items(): - cols.extend(get_onehot_cols(col, values)) - return cols - - -@dataclass -class LNPDatasetConfig: - """数据集配置""" - comp_cols: List[str] = None - phys_cols: List[str] = None - help_cols: List[str] = None - exp_cols: List[str] = None - - def __post_init__(self): - self.comp_cols = self.comp_cols or COMP_COLS - self.phys_cols = self.phys_cols or get_phys_cols() - self.help_cols = self.help_cols or HELP_COLS - self.exp_cols = self.exp_cols or get_exp_cols() - - -class LNPDataset(Dataset): - """ - LNP 数据集，用于 PyTorch DataLoader。 - - 返回: - - smiles: str - - tabular: Dict[str, Tensor] with keys "comp", "phys", "help", "exp" - - targets: Dict[str, Tensor] with keys "size", "pdi", "ee", "delivery", "biodist", "toxic" - - mask: Dict[str, Tensor] 标记哪些 target 有效（非缺失） - """ - - def __init__( - self, - df: pd.DataFrame, - config: Optional[LNPDatasetConfig] = None, - ): - self.config = config or LNPDatasetConfig() - self.df = process_dataframe(df) - - # 提取数据 - self.smiles = self.df[SMILES_COL].tolist() - - # Tabular features - self.comp = self.df[self.config.comp_cols].values.astype(np.float32) - self.phys = self.df[self.config.phys_cols].values.astype(np.float32) - self.help = self.df[self.config.help_cols].values.astype(np.float32) - self.exp = self.df[self.config.exp_cols].values.astype(np.float32) - - # Targets - self.size = self.df["size"].values.astype(np.float32) if "size" in self.df.columns else None - self.delivery = self.df["quantified_delivery"].values.astype(np.float32) if "quantified_delivery" in self.df.columns else None - self.toxic = self.df[TARGET_TOXIC].values.astype(np.int64) if TARGET_TOXIC in self.df.columns else None - - # PDI: one-hot -> class index - if all(col in self.df.columns for col in TARGET_CLASSIFICATION_PDI): - pdi_onehot = self.df[TARGET_CLASSIFICATION_PDI].values - self.pdi = np.argmax(pdi_onehot, axis=1).astype(np.int64) - self.pdi_valid = pdi_onehot.sum(axis=1) > 0 - else: - self.pdi = None - self.pdi_valid = None - - # EE: one-hot -> class index - if all(col in self.df.columns for col in TARGET_CLASSIFICATION_EE): - ee_onehot = self.df[TARGET_CLASSIFICATION_EE].values - self.ee = np.argmax(ee_onehot, axis=1).astype(np.int64) - self.ee_valid = ee_onehot.sum(axis=1) > 0 - else: - self.ee = None - self.ee_valid = None - - # Biodistribution - if all(col in self.df.columns for col in TARGET_BIODIST): - self.biodist = self.df[TARGET_BIODIST].values.astype(np.float32) - self.biodist_valid = self.biodist.sum(axis=1) > 0 - else: - self.biodist = None - self.biodist_valid = None - - def __len__(self) -> int: - return len(self.smiles) - - def __getitem__(self, idx: int) -> Dict: - item = { - "smiles": self.smiles[idx], - "tabular": { - "comp": torch.from_numpy(self.comp[idx]), - "phys": torch.from_numpy(self.phys[idx]), - "help": torch.from_numpy(self.help[idx]), - "exp": torch.from_numpy(self.exp[idx]), - }, - "targets": {}, - "mask": {}, - } - - # Targets and masks - if self.size is not None: - item["targets"]["size"] = torch.tensor(self.size[idx], dtype=torch.float32) - item["mask"]["size"] = torch.tensor(not np.isnan(self.size[idx]), dtype=torch.bool) - - if self.delivery is not None: - item["targets"]["delivery"] = torch.tensor(self.delivery[idx], dtype=torch.float32) - item["mask"]["delivery"] = torch.tensor(not np.isnan(self.delivery[idx]), dtype=torch.bool) - - if self.toxic is not None: - item["targets"]["toxic"] = torch.tensor(self.toxic[idx], dtype=torch.long) - item["mask"]["toxic"] = torch.tensor(self.toxic[idx] >= 0, dtype=torch.bool) - - if self.pdi is not None: - item["targets"]["pdi"] = torch.tensor(self.pdi[idx], dtype=torch.long) - item["mask"]["pdi"] = torch.tensor(self.pdi_valid[idx], dtype=torch.bool) - - if self.ee is not None: - item["targets"]["ee"] = torch.tensor(self.ee[idx], dtype=torch.long) - item["mask"]["ee"] = torch.tensor(self.ee_valid[idx], dtype=torch.bool) - - if self.biodist is not None: - item["targets"]["biodist"] = torch.from_numpy(self.biodist[idx]) - item["mask"]["biodist"] = torch.tensor(self.biodist_valid[idx], dtype=torch.bool) - - return item - - -def collate_fn(batch: List[Dict]) -> Dict: - """ - 自定义 collate 函数，用于 DataLoader。 - - Returns: - - smiles: List[str] - - tabular: Dict[str, Tensor] with batched tensors - - targets: Dict[str, Tensor] with batched tensors - - mask: Dict[str, Tensor] with batched masks - """ - smiles = [item["smiles"] for item in batch] - - tabular = { - key: torch.stack([item["tabular"][key] for item in batch]) - for key in batch[0]["tabular"].keys() - } - - targets = {} - mask = {} - for key in batch[0]["targets"].keys(): - targets[key] = torch.stack([item["targets"][key] for item in batch]) - mask[key] = torch.stack([item["mask"][key] for item in batch]) - - return { - "smiles": smiles, - "tabular": tabular, - "targets": targets, - "mask": mask, - } - - -def load_dataset( - path: Path, - train_ratio: float = 0.8, - val_ratio: float = 0.1, - seed: int = 42, -) -> Tuple[LNPDataset, LNPDataset, LNPDataset]: - """ - 加载并划分数据集。 - - Args: - path: CSV 文件路径 - train_ratio: 训练集比例 - val_ratio: 验证集比例 - seed: 随机种子 - - Returns: - (train_dataset, val_dataset, test_dataset) - """ - df = pd.read_csv(path) - - # 随机打乱 - df = df.sample(frac=1, random_state=seed).reset_index(drop=True) - - n = len(df) - n_train = int(n * train_ratio) - n_val = int(n * val_ratio) - - train_df = df.iloc[:n_train] - val_df = df.iloc[n_train:n_train + n_val] - test_df = df.iloc[n_train + n_val:] - - config = LNPDatasetConfig() - - return ( - LNPDataset(train_df, config), - LNPDataset(val_df, config), - LNPDataset(test_df, config), - ) - - -# ============ 外部数据（仅 delivery）处理 ============ - -# 外部数据中 Value_name 的值映射（空格 -> 下划线） -EXTERNAL_VALUE_NAME_MAP = { - "log luminescence": "log_luminescence", - "Luminescence": "luminescence", - "FFL silencing": "FFL_silencing", - "Peptide abundance": "Peptide_abundance", - "hEPO": "hEPO", - "FVII silencing": "FVII_silencing", - "GFP delivery": "GFP_delivery", - "Discretized luminescence": "Discretized_luminescence", -} - -# 外部数据中 Mix_type 的值映射 -EXTERNAL_MIX_TYPE_MAP = { - "Hand": "Pipetting", # 外部数据用 "Hand" 表示 "Pipetting" - "Microfluidic": "Microfluidic", - "Pipetting": "Pipetting", -} - - -def process_external_dataframe(df: pd.DataFrame) -> pd.DataFrame: - """ - 处理外部 LiON 数据的 DataFrame，对齐到模型所需的列格式。 - - 与 process_dataframe 类似，但针对外部数据的列名差异做适配： - - Value_name 值中的空格需要转为下划线 - - Mix_type 中 "Hand" 需要映射为 "Pipetting" - """ - df = df.copy() - - # 1. 预处理：映射 Value_name 和 Mix_type 的值 - if "Value_name" in df.columns: - df["Value_name"] = df["Value_name"].map( - lambda x: EXTERNAL_VALUE_NAME_MAP.get(x, x) if pd.notna(x) else x - ) - - if "Mix_type" in df.columns: - df["Mix_type"] = df["Mix_type"].map( - lambda x: EXTERNAL_MIX_TYPE_MAP.get(x, x) if pd.notna(x) else x - ) - - # 2. 处理 phys token 的 one-hot 列（如果不存在则从原始列生成） - for col, values in PHYS_ONEHOT_SPECS.items(): - for v in values: - onehot_col = f"{col}_{v}" - if onehot_col not in df.columns: - if col in df.columns: - df[onehot_col] = (df[col] == v).astype(float) - else: - df[onehot_col] = 0.0 - - # 3. 处理 exp token 的 one-hot 列 - # 外部数据部分列已存在（如 Model_type_*, Delivery_target_* 等），但可能缺失某些类别 - for col, values in EXP_ONEHOT_SPECS.items(): - for v in values: - onehot_col = f"{col}_{v}" - if onehot_col not in df.columns: - if col in df.columns: - df[onehot_col] = (df[col] == v).astype(float) - else: - df[onehot_col] = 0.0 - else: - # 确保是 float 类型 - df[onehot_col] = df[onehot_col].fillna(0.0).astype(float) - - # 4. 确保 comp 列存在且为 float - for col in COMP_COLS: - if col in df.columns: - df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0.0) - else: - df[col] = 0.0 - - # 5. 确保 help 列存在 - for col in HELP_COLS: - if col not in df.columns: - df[col] = 0.0 - else: - df[col] = df[col].fillna(0.0).astype(float) - - # 6. 处理 quantified_delivery - if "quantified_delivery" in df.columns: - df["quantified_delivery"] = pd.to_numeric(df["quantified_delivery"], errors="coerce") - - return df - - -class ExternalDeliveryDataset(Dataset): - """ - 外部 LiON 数据集，仅用于 delivery 预训练。 - - 返回: - - smiles: str - - tabular: Dict[str, Tensor] with keys "comp", "phys", "help", "exp" - - targets: Dict[str, Tensor] with key "delivery" - - mask: Dict[str, Tensor] with key "delivery" - """ - - def __init__( - self, - df: pd.DataFrame, - config: Optional[LNPDatasetConfig] = None, - ): - self.config = config or LNPDatasetConfig() - self.df = process_external_dataframe(df) - - # 提取数据 - self.smiles = self.df[SMILES_COL].tolist() - - # Tabular features - self.comp = self.df[self.config.comp_cols].values.astype(np.float32) - self.phys = self.df[self.config.phys_cols].values.astype(np.float32) - self.help = self.df[self.config.help_cols].values.astype(np.float32) - self.exp = self.df[self.config.exp_cols].values.astype(np.float32) - - # 只有 delivery 作为 target - self.delivery = self.df["quantified_delivery"].values.astype(np.float32) if "quantified_delivery" in self.df.columns else None - - def __len__(self) -> int: - return len(self.smiles) - - def __getitem__(self, idx: int) -> Dict: - item = { - "smiles": self.smiles[idx], - "tabular": { - "comp": torch.from_numpy(self.comp[idx]), - "phys": torch.from_numpy(self.phys[idx]), - "help": torch.from_numpy(self.help[idx]), - "exp": torch.from_numpy(self.exp[idx]), - }, - "targets": {}, - "mask": {}, - } - - # delivery target and mask - if self.delivery is not None: - item["targets"]["delivery"] = torch.tensor(self.delivery[idx], dtype=torch.float32) - item["mask"]["delivery"] = torch.tensor(not np.isnan(self.delivery[idx]), dtype=torch.bool) - - return item - - -def load_external_dataset( - path: Path, - train_ratio: float = 0.9, - seed: int = 42, -) -> Tuple[ExternalDeliveryDataset, ExternalDeliveryDataset]: - """ - 加载外部 LiON 数据集并划分为 train/val。 - - Args: - path: CSV 文件路径 - train_ratio: 训练集比例（剩余为验证集） - seed: 随机种子 - - Returns: - (train_dataset, val_dataset) - """ - df = pd.read_csv(path) - - # 过滤掉 quantified_delivery 为空的行 - if "quantified_delivery" in df.columns: - df = df[df["quantified_delivery"].notna()].reset_index(drop=True) - - # 随机打乱 - df = df.sample(frac=1, random_state=seed).reset_index(drop=True) - - n = len(df) - n_train = int(n * train_ratio) - - train_df = df.iloc[:n_train] - val_df = df.iloc[n_train:] - - config = LNPDatasetConfig() - - return ( - ExternalDeliveryDataset(train_df, config), - ExternalDeliveryDataset(val_df, config), - ) +"""数据集处理模块""" + +from pathlib import Path +from typing import Dict, List, Tuple, Optional +from dataclasses import dataclass + +import numpy as np +import pandas as pd +import torch +from torch.utils.data import Dataset + + +# ============ 列名配置 ============ + +# SMILES 列 +SMILES_COL = "smiles" + +# comp token: 配方比例 [5] +COMP_COLS = [ + "Cationic_Lipid_to_mRNA_weight_ratio", + "Cationic_Lipid_Mol_Ratio", + "Phospholipid_Mol_Ratio", + "Cholesterol_Mol_Ratio", + "PEG_Lipid_Mol_Ratio", +] + +# phys token: 物理/实验参数 one-hot [12] +# 需要从原始列生成 one-hot +PHYS_ONEHOT_SPECS = { + "Purity": ["Pure", "Crude"], + "Mix_type": ["Microfluidic", "Pipetting"], + "Cargo_type": ["mRNA", "pDNA", "siRNA"], + "Target_or_delivered_gene": ["FFL", "Peptide_barcode", "hEPO", "FVII", "GFP"], +} + +# help token: Helper lipid one-hot [4] +HELP_COLS = [ + "Helper_lipid_ID_DOPE", + "Helper_lipid_ID_DOTAP", + "Helper_lipid_ID_DSPC", + "Helper_lipid_ID_MDOA", +] + +# exp token: 实验条件 one-hot [32] +EXP_ONEHOT_SPECS = { + "Model_type": ["A549", "BDMC", "BMDM", "HBEC_ALI", "HEK293T", "HeLa", "IGROV1", "Mouse", "RAW264p7"], + "Delivery_target": ["body", "dendritic_cell", "generic_cell", "liver", "lung", "lung_epithelium", "macrophage", "muscle", "spleen"], + "Route_of_administration": ["in_vitro", "intramuscular", "intratracheal", "intravenous"], + "Batch_or_individual_or_barcoded": ["Barcoded", "Individual"], + "Value_name": ["log_luminescence", "luminescence", "FFL_silencing", "Peptide_abundance", "hEPO", "FVII_silencing", "GFP_delivery", "Discretized_luminescence"], +} + +# Target 列 +TARGET_REGRESSION = ["size", "quantified_delivery"] +TARGET_CLASSIFICATION_PDI = ["PDI_0_0to0_2", "PDI_0_2to0_3", "PDI_0_3to0_4", "PDI_0_4to0_5"] +TARGET_CLASSIFICATION_EE = ["Encapsulation_Efficiency_EE<50", "Encapsulation_Efficiency_50<=EE<80", "Encapsulation_Efficiency_80 List[str]: + """生成 one-hot 列名""" + return [f"{prefix}_{v}" for v in values] + + +def process_dataframe(df: pd.DataFrame) -> pd.DataFrame: + """ + 处理原始 DataFrame，生成模型所需的所有列。 + + Args: + df: 原始 DataFrame + + Returns: + 处理后的 DataFrame，包含所有需要的列 + """ + df = df.copy() + + # 1. 处理 phys token 的 one-hot 列（如果不存在则生成） + for col, values in PHYS_ONEHOT_SPECS.items(): + for v in values: + onehot_col = f"{col}_{v}" + if onehot_col not in df.columns: + if col in df.columns: + df[onehot_col] = (df[col] == v).astype(float) + else: + df[onehot_col] = 0.0 + + # 2. 处理 exp token 的 one-hot 列（如果不存在则生成） + for col, values in EXP_ONEHOT_SPECS.items(): + for v in values: + onehot_col = f"{col}_{v}" + if onehot_col not in df.columns: + if col in df.columns: + df[onehot_col] = (df[col] == v).astype(float) + else: + df[onehot_col] = 0.0 + + # 3. 确保 comp 列存在且为 float + for col in COMP_COLS: + if col in df.columns: + df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0.0) + else: + df[col] = 0.0 + + # 4. 确保 help 列存在 + for col in HELP_COLS: + if col not in df.columns: + df[col] = 0.0 + else: + df[col] = df[col].fillna(0.0).astype(float) + + # 5. 处理 target 列 + # size: 已经 log 过，填充缺失值 + if "size" in df.columns: + df["size"] = pd.to_numeric(df["size"], errors="coerce") + + # quantified_delivery: 已经 z-score 过 + if "quantified_delivery" in df.columns: + df["quantified_delivery"] = pd.to_numeric(df["quantified_delivery"], errors="coerce") + + # toxic: 0/1 + if TARGET_TOXIC in df.columns: + df[TARGET_TOXIC] = pd.to_numeric(df[TARGET_TOXIC], errors="coerce").fillna(-1).astype(int) + + # PDI 和 EE 的 one-hot 分类 + for col in TARGET_CLASSIFICATION_PDI + TARGET_CLASSIFICATION_EE: + if col in df.columns: + df[col] = df[col].fillna(0).astype(float) + + # Biodistribution + for col in TARGET_BIODIST: + if col in df.columns: + df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0.0) + if all(col in df.columns for col in TARGET_BIODIST): + bd = df[TARGET_BIODIST].values.astype(float) + s = bd.sum(axis=1, keepdims=True) + nz = s.squeeze(-1) > 0 + bd[nz] = bd[nz] / s[nz] + df[TARGET_BIODIST] = bd + + return df + + +def get_phys_cols() -> List[str]: + """获取 phys token 的所有列名""" + cols = [] + for col, values in PHYS_ONEHOT_SPECS.items(): + cols.extend(get_onehot_cols(col, values)) + return cols + + +def get_exp_cols() -> List[str]: + """获取 exp token 的所有列名""" + cols = [] + for col, values in EXP_ONEHOT_SPECS.items(): + cols.extend(get_onehot_cols(col, values)) + return cols + + +@dataclass +class LNPDatasetConfig: + """数据集配置""" + comp_cols: List[str] = None + phys_cols: List[str] = None + help_cols: List[str] = None + exp_cols: List[str] = None + + def __post_init__(self): + self.comp_cols = self.comp_cols or COMP_COLS + self.phys_cols = self.phys_cols or get_phys_cols() + self.help_cols = self.help_cols or HELP_COLS + self.exp_cols = self.exp_cols or get_exp_cols() + + +class LNPDataset(Dataset): + """ + LNP 数据集，用于 PyTorch DataLoader。 + + 返回: + - smiles: str + - tabular: Dict[str, Tensor] with keys "comp", "phys", "help", "exp" + - targets: Dict[str, Tensor] with keys "size", "pdi", "ee", "delivery", "biodist", "toxic" + - mask: Dict[str, Tensor] 标记哪些 target 有效（非缺失） + """ + + def __init__( + self, + df: pd.DataFrame, + config: Optional[LNPDatasetConfig] = None, + ): + self.config = config or LNPDatasetConfig() + self.df = process_dataframe(df) + + # 提取数据 + self.smiles = self.df[SMILES_COL].tolist() + + # Tabular features + self.comp = self.df[self.config.comp_cols].values.astype(np.float32) + self.phys = self.df[self.config.phys_cols].values.astype(np.float32) + self.help = self.df[self.config.help_cols].values.astype(np.float32) + self.exp = self.df[self.config.exp_cols].values.astype(np.float32) + + # Targets + self.size = self.df["size"].values.astype(np.float32) if "size" in self.df.columns else None + self.delivery = self.df["quantified_delivery"].values.astype(np.float32) if "quantified_delivery" in self.df.columns else None + self.toxic = self.df[TARGET_TOXIC].values.astype(np.int64) if TARGET_TOXIC in self.df.columns else None + + # PDI: one-hot -> class index + if all(col in self.df.columns for col in TARGET_CLASSIFICATION_PDI): + pdi_onehot = self.df[TARGET_CLASSIFICATION_PDI].values + self.pdi = np.argmax(pdi_onehot, axis=1).astype(np.int64) + self.pdi_valid = pdi_onehot.sum(axis=1) > 0 + else: + self.pdi = None + self.pdi_valid = None + + # EE: one-hot -> class index + if all(col in self.df.columns for col in TARGET_CLASSIFICATION_EE): + ee_onehot = self.df[TARGET_CLASSIFICATION_EE].values + self.ee = np.argmax(ee_onehot, axis=1).astype(np.int64) + self.ee_valid = ee_onehot.sum(axis=1) > 0 + else: + self.ee = None + self.ee_valid = None + + # Biodistribution + if all(col in self.df.columns for col in TARGET_BIODIST): + self.biodist = self.df[TARGET_BIODIST].values.astype(np.float32) + self.biodist_valid = self.biodist.sum(axis=1) > 0 + else: + self.biodist = None + self.biodist_valid = None + + def __len__(self) -> int: + return len(self.smiles) + + def __getitem__(self, idx: int) -> Dict: + item = { + "smiles": self.smiles[idx], + "tabular": { + "comp": torch.from_numpy(self.comp[idx]), + "phys": torch.from_numpy(self.phys[idx]), + "help": torch.from_numpy(self.help[idx]), + "exp": torch.from_numpy(self.exp[idx]), + }, + "targets": {}, + "mask": {}, + } + + # Targets and masks + if self.size is not None: + item["targets"]["size"] = torch.tensor(self.size[idx], dtype=torch.float32) + item["mask"]["size"] = torch.tensor(not np.isnan(self.size[idx]), dtype=torch.bool) + + if self.delivery is not None: + item["targets"]["delivery"] = torch.tensor(self.delivery[idx], dtype=torch.float32) + item["mask"]["delivery"] = torch.tensor(not np.isnan(self.delivery[idx]), dtype=torch.bool) + + if self.toxic is not None: + item["targets"]["toxic"] = torch.tensor(self.toxic[idx], dtype=torch.long) + item["mask"]["toxic"] = torch.tensor(self.toxic[idx] >= 0, dtype=torch.bool) + + if self.pdi is not None: + item["targets"]["pdi"] = torch.tensor(self.pdi[idx], dtype=torch.long) + item["mask"]["pdi"] = torch.tensor(self.pdi_valid[idx], dtype=torch.bool) + + if self.ee is not None: + item["targets"]["ee"] = torch.tensor(self.ee[idx], dtype=torch.long) + item["mask"]["ee"] = torch.tensor(self.ee_valid[idx], dtype=torch.bool) + + if self.biodist is not None: + item["targets"]["biodist"] = torch.from_numpy(self.biodist[idx]) + item["mask"]["biodist"] = torch.tensor(self.biodist_valid[idx], dtype=torch.bool) + + return item + + +def collate_fn(batch: List[Dict]) -> Dict: + """ + 自定义 collate 函数，用于 DataLoader。 + + Returns: + - smiles: List[str] + - tabular: Dict[str, Tensor] with batched tensors + - targets: Dict[str, Tensor] with batched tensors + - mask: Dict[str, Tensor] with batched masks + """ + smiles = [item["smiles"] for item in batch] + + tabular = { + key: torch.stack([item["tabular"][key] for item in batch]) + for key in batch[0]["tabular"].keys() + } + + targets = {} + mask = {} + for key in batch[0]["targets"].keys(): + targets[key] = torch.stack([item["targets"][key] for item in batch]) + mask[key] = torch.stack([item["mask"][key] for item in batch]) + + return { + "smiles": smiles, + "tabular": tabular, + "targets": targets, + "mask": mask, + } + + +def load_dataset( + path: Path, + train_ratio: float = 0.8, + val_ratio: float = 0.1, + seed: int = 42, +) -> Tuple[LNPDataset, LNPDataset, LNPDataset]: + """ + 加载并划分数据集。 + + Args: + path: CSV 文件路径 + train_ratio: 训练集比例 + val_ratio: 验证集比例 + seed: 随机种子 + + Returns: + (train_dataset, val_dataset, test_dataset) + """ + df = pd.read_csv(path) + + # 随机打乱 + df = df.sample(frac=1, random_state=seed).reset_index(drop=True) + + n = len(df) + n_train = int(n * train_ratio) + n_val = int(n * val_ratio) + + train_df = df.iloc[:n_train] + val_df = df.iloc[n_train:n_train + n_val] + test_df = df.iloc[n_train + n_val:] + + config = LNPDatasetConfig() + + return ( + LNPDataset(train_df, config), + LNPDataset(val_df, config), + LNPDataset(test_df, config), + ) + + +# ============ 外部数据（仅 delivery）处理 ============ + +# 外部数据中 Value_name 的值映射（空格 -> 下划线） +EXTERNAL_VALUE_NAME_MAP = { + "log luminescence": "log_luminescence", + "Luminescence": "luminescence", + "FFL silencing": "FFL_silencing", + "Peptide abundance": "Peptide_abundance", + "hEPO": "hEPO", + "FVII silencing": "FVII_silencing", + "GFP delivery": "GFP_delivery", + "Discretized luminescence": "Discretized_luminescence", +} + +# 外部数据中 Mix_type 的值映射 +EXTERNAL_MIX_TYPE_MAP = { + "Hand": "Pipetting", # 外部数据用 "Hand" 表示 "Pipetting" + "Microfluidic": "Microfluidic", + "Pipetting": "Pipetting", +} + + +def process_external_dataframe(df: pd.DataFrame) -> pd.DataFrame: + """ + 处理外部 LiON 数据的 DataFrame，对齐到模型所需的列格式。 + + 与 process_dataframe 类似，但针对外部数据的列名差异做适配： + - Value_name 值中的空格需要转为下划线 + - Mix_type 中 "Hand" 需要映射为 "Pipetting" + """ + df = df.copy() + + # 1. 预处理：映射 Value_name 和 Mix_type 的值 + if "Value_name" in df.columns: + df["Value_name"] = df["Value_name"].map( + lambda x: EXTERNAL_VALUE_NAME_MAP.get(x, x) if pd.notna(x) else x + ) + + if "Mix_type" in df.columns: + df["Mix_type"] = df["Mix_type"].map( + lambda x: EXTERNAL_MIX_TYPE_MAP.get(x, x) if pd.notna(x) else x + ) + + # 2. 处理 phys token 的 one-hot 列（如果不存在则从原始列生成） + for col, values in PHYS_ONEHOT_SPECS.items(): + for v in values: + onehot_col = f"{col}_{v}" + if onehot_col not in df.columns: + if col in df.columns: + df[onehot_col] = (df[col] == v).astype(float) + else: + df[onehot_col] = 0.0 + + # 3. 处理 exp token 的 one-hot 列 + # 外部数据部分列已存在（如 Model_type_*, Delivery_target_* 等），但可能缺失某些类别 + for col, values in EXP_ONEHOT_SPECS.items(): + for v in values: + onehot_col = f"{col}_{v}" + if onehot_col not in df.columns: + if col in df.columns: + df[onehot_col] = (df[col] == v).astype(float) + else: + df[onehot_col] = 0.0 + else: + # 确保是 float 类型 + df[onehot_col] = df[onehot_col].fillna(0.0).astype(float) + + # 4. 确保 comp 列存在且为 float + for col in COMP_COLS: + if col in df.columns: + df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0.0) + else: + df[col] = 0.0 + + # 5. 确保 help 列存在 + for col in HELP_COLS: + if col not in df.columns: + df[col] = 0.0 + else: + df[col] = df[col].fillna(0.0).astype(float) + + # 6. 处理 quantified_delivery + if "quantified_delivery" in df.columns: + df["quantified_delivery"] = pd.to_numeric(df["quantified_delivery"], errors="coerce") + + return df + + +class ExternalDeliveryDataset(Dataset): + """ + 外部 LiON 数据集，仅用于 delivery 预训练。 + + 返回: + - smiles: str + - tabular: Dict[str, Tensor] with keys "comp", "phys", "help", "exp" + - targets: Dict[str, Tensor] with key "delivery" + - mask: Dict[str, Tensor] with key "delivery" + """ + + def __init__( + self, + df: pd.DataFrame, + config: Optional[LNPDatasetConfig] = None, + ): + self.config = config or LNPDatasetConfig() + self.df = process_external_dataframe(df) + + # 提取数据 + self.smiles = self.df[SMILES_COL].tolist() + + # Tabular features + self.comp = self.df[self.config.comp_cols].values.astype(np.float32) + self.phys = self.df[self.config.phys_cols].values.astype(np.float32) + self.help = self.df[self.config.help_cols].values.astype(np.float32) + self.exp = self.df[self.config.exp_cols].values.astype(np.float32) + + # 只有 delivery 作为 target + self.delivery = self.df["quantified_delivery"].values.astype(np.float32) if "quantified_delivery" in self.df.columns else None + + def __len__(self) -> int: + return len(self.smiles) + + def __getitem__(self, idx: int) -> Dict: + item = { + "smiles": self.smiles[idx], + "tabular": { + "comp": torch.from_numpy(self.comp[idx]), + "phys": torch.from_numpy(self.phys[idx]), + "help": torch.from_numpy(self.help[idx]), + "exp": torch.from_numpy(self.exp[idx]), + }, + "targets": {}, + "mask": {}, + } + + # delivery target and mask + if self.delivery is not None: + item["targets"]["delivery"] = torch.tensor(self.delivery[idx], dtype=torch.float32) + item["mask"]["delivery"] = torch.tensor(not np.isnan(self.delivery[idx]), dtype=torch.bool) + + return item + + +def load_external_dataset( + path: Path, + train_ratio: float = 0.9, + seed: int = 42, +) -> Tuple[ExternalDeliveryDataset, ExternalDeliveryDataset]: + """ + 加载外部 LiON 数据集并划分为 train/val。 + + Args: + path: CSV 文件路径 + train_ratio: 训练集比例（剩余为验证集） + seed: 随机种子 + + Returns: + (train_dataset, val_dataset) + """ + df = pd.read_csv(path) + + # 过滤掉 quantified_delivery 为空的行 + if "quantified_delivery" in df.columns: + df = df[df["quantified_delivery"].notna()].reset_index(drop=True) + + # 随机打乱 + df = df.sample(frac=1, random_state=seed).reset_index(drop=True) + + n = len(df) + n_train = int(n * train_ratio) + + train_df = df.iloc[:n_train] + val_df = df.iloc[n_train:] + + config = LNPDatasetConfig() + + return ( + ExternalDeliveryDataset(train_df, config), + ExternalDeliveryDataset(val_df, config), + ) diff --git a/lnp_ml/modeling/heads.py b/lnp_ml/modeling/heads.py index b0cdb3d..44d2986 100644 --- a/lnp_ml/modeling/heads.py +++ b/lnp_ml/modeling/heads.py @@ -1,118 +1,124 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Dict - - -class RegressionHead(nn.Module): - """回归任务头：输出单个 float 值""" - - def __init__(self, in_dim: int, hidden_dim: int = 128, dropout: float = 0.1) -> None: - super().__init__() - self.net = nn.Sequential( - nn.Linear(in_dim, hidden_dim), - nn.ReLU(), - nn.Dropout(dropout), - nn.Linear(hidden_dim, 1), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """[B, in_dim] -> [B, 1]""" - return self.net(x) - - -class ClassificationHead(nn.Module): - """分类任务头：输出 logits""" - - def __init__( - self, in_dim: int, num_classes: int, hidden_dim: int = 128, dropout: float = 0.1 - ) -> None: - super().__init__() - self.net = nn.Sequential( - nn.Linear(in_dim, hidden_dim), - nn.ReLU(), - nn.Dropout(dropout), - nn.Linear(hidden_dim, num_classes), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """[B, in_dim] -> [B, num_classes] (logits)""" - return self.net(x) - - -class DistributionHead(nn.Module): - """分布任务头：输出和为 1 的概率分布（用于 Biodistribution）""" - - def __init__( - self, in_dim: int, num_outputs: int, hidden_dim: int = 128, dropout: float = 0.1 - ) -> None: - super().__init__() - self.net = nn.Sequential( - nn.Linear(in_dim, hidden_dim), - nn.ReLU(), - nn.Dropout(dropout), - nn.Linear(hidden_dim, num_outputs), - ) - - def forward(self, x: torch.Tensor) -> torch.Tensor: - """[B, in_dim] -> [B, num_outputs] (softmax, sum=1)""" - logits = self.net(x) - return F.softmax(logits, dim=-1) - - -class MultiTaskHead(nn.Module): - """ - 多任务预测头，根据任务配置自动创建对应的子头。 - - 输出: - - size: [B, 1] 回归 - - pdi: [B, 4] 分类 logits - - ee: [B, 3] 分类 logits - - delivery: [B, 1] 回归 - - biodist: [B, 7] softmax 分布 - - toxic: [B, 2] 二分类 logits - """ - - def __init__(self, in_dim: int, hidden_dim: int = 128, dropout: float = 0.1) -> None: - super().__init__() - - # size: 回归 (log-transformed) - self.size_head = RegressionHead(in_dim, hidden_dim, dropout) - - # PDI: 4 分类 - self.pdi_head = ClassificationHead(in_dim, num_classes=4, hidden_dim=hidden_dim, dropout=dropout) - - # Encapsulation Efficiency: 3 分类 - self.ee_head = ClassificationHead(in_dim, num_classes=3, hidden_dim=hidden_dim, dropout=dropout) - - # quantified_delivery: 回归 (z-scored) - self.delivery_head = RegressionHead(in_dim, hidden_dim, dropout) - - # Biodistribution: 7 输出，softmax (sum=1) - self.biodist_head = DistributionHead(in_dim, num_outputs=7, hidden_dim=hidden_dim, dropout=dropout) - - # toxic: 二分类 - self.toxic_head = ClassificationHead(in_dim, num_classes=2, hidden_dim=hidden_dim, dropout=dropout) - - def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]: - """ - Args: - x: [B, in_dim] fusion 层输出 - - Returns: - Dict with keys: - - "size": [B, 1] - - "pdi": [B, 4] logits - - "ee": [B, 3] logits - - "delivery": [B, 1] - - "biodist": [B, 7] probabilities (sum=1) - - "toxic": [B, 2] logits - """ - return { - "size": self.size_head(x), - "pdi": self.pdi_head(x), - "ee": self.ee_head(x), - "delivery": self.delivery_head(x), - "biodist": self.biodist_head(x), - "toxic": self.toxic_head(x), - } +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Dict + + +class RegressionHead(nn.Module): + """回归任务头：输出单个 float 值""" + + def __init__(self, in_dim: int, hidden_dim: int = 128, dropout: float = 0.1) -> None: + super().__init__() + self.net = nn.Sequential( + nn.Linear(in_dim, hidden_dim), + nn.ReLU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, 1), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """[B, in_dim] -> [B, 1]""" + return self.net(x) + + +class ClassificationHead(nn.Module): + """分类任务头：输出 logits""" + + def __init__( + self, in_dim: int, num_classes: int, hidden_dim: int = 128, dropout: float = 0.1 + ) -> None: + super().__init__() + self.net = nn.Sequential( + nn.Linear(in_dim, hidden_dim), + nn.ReLU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, num_classes), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """[B, in_dim] -> [B, num_classes] (logits)""" + return self.net(x) + + +class DistributionHead(nn.Module): + """分布任务头：输出和为 1 的概率分布（用于 Biodistribution）""" + + def __init__( + self, in_dim: int, num_outputs: int, hidden_dim: int = 128, dropout: float = 0.1 + ) -> None: + super().__init__() + self.net = nn.Sequential( + nn.Linear(in_dim, hidden_dim), + nn.ReLU(), + nn.Dropout(dropout), + nn.Linear(hidden_dim, num_outputs), + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """[B, in_dim] -> [B, num_outputs] (softmax, sum=1)""" + logits = self.net(x) + return F.softmax(logits, dim=-1) + + +class MultiTaskHead(nn.Module): + """ + 多任务预测头，根据任务配置自动创建对应的子头。 + + 输出: + - size: [B, 1] 回归 + - pdi: [B, 4] 分类 logits + - ee: [B, 3] 分类 logits + - delivery: [B, 1] 回归 + - biodist: [B, 7] softmax 分布 + - toxic: [B, 2] 二分类 logits + """ + + def __init__(self, in_dim: int, hidden_dim: int = 128, dropout: float = 0.1) -> None: + super().__init__() + + # size: 回归 (log-transformed) + self.size_head = RegressionHead(in_dim, hidden_dim, dropout) + + # PDI: 4 分类 + self.pdi_head = ClassificationHead(in_dim, num_classes=4, hidden_dim=hidden_dim, dropout=dropout) + + # Encapsulation Efficiency: 3 分类 + self.ee_head = ClassificationHead(in_dim, num_classes=3, hidden_dim=hidden_dim, dropout=dropout) + + # quantified_delivery: 回归 (z-scored) + self.delivery_head = RegressionHead(in_dim, hidden_dim, dropout) + + # Biodistribution: 7 输出，softmax (sum=1) + self.biodist_head = DistributionHead(in_dim, num_outputs=7, hidden_dim=hidden_dim, dropout=dropout) + + # toxic: 二分类 + self.toxic_head = ClassificationHead(in_dim, num_classes=2, hidden_dim=hidden_dim, dropout=dropout) + + # 不确定性加权（Kendall 2018）：每个任务一个可学习 log σ²，初始 0 + self.log_vars = nn.ParameterDict({ + t: nn.Parameter(torch.zeros(())) + for t in ["size", "delivery", "pdi", "ee", "toxic", "biodist"] + }) + + def forward(self, x: torch.Tensor) -> Dict[str, torch.Tensor]: + """ + Args: + x: [B, in_dim] fusion 层输出 + + Returns: + Dict with keys: + - "size": [B, 1] + - "pdi": [B, 4] logits + - "ee": [B, 3] logits + - "delivery": [B, 1] + - "biodist": [B, 7] probabilities (sum=1) + - "toxic": [B, 2] logits + """ + return { + "size": self.size_head(x), + "pdi": self.pdi_head(x), + "ee": self.ee_head(x), + "delivery": self.delivery_head(x), + "biodist": self.biodist_head(x), + "toxic": self.toxic_head(x), + } diff --git a/lnp_ml/modeling/layers/fusion.py b/lnp_ml/modeling/layers/fusion.py index 82c7636..70ef066 100644 --- a/lnp_ml/modeling/layers/fusion.py +++ b/lnp_ml/modeling/layers/fusion.py @@ -1,111 +1,152 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from typing import Dict, Literal, Tuple, Union - - -PoolingStrategy = Literal["concat", "avg", "max", "attention"] - - -class FusionLayer(nn.Module): - """ - 将多个 token 融合成单个向量。 - - 输入: Dict[str, Tensor] 或 [B, n_tokens, d_model] - 输出: [B, fusion_dim] - - 策略: - - concat: [B, n_tokens, d_model] -> [B, n_tokens * d_model] - - avg: [B, n_tokens, d_model] -> [B, d_model] - - max: [B, n_tokens, d_model] -> [B, d_model] - - attention: [B, n_tokens, d_model] -> [B, d_model] (learnable attention pooling) - """ - - def __init__( - self, - d_model: int, - n_tokens: int, - strategy: PoolingStrategy = "attention", - ) -> None: - """ - Args: - d_model: 每个 token 的维度 - n_tokens: token 数量（如 8） - strategy: 融合策略 - """ - super().__init__() - self.d_model = d_model - self.n_tokens = n_tokens - self.strategy = strategy - - if strategy == "concat": - self.fusion_dim = n_tokens * d_model - else: - self.fusion_dim = d_model - - # Attention pooling: learnable query - if strategy == "attention": - self.attn_query = nn.Parameter(torch.randn(1, 1, d_model)) - self.attn_proj = nn.Linear(d_model, d_model) - - def forward( - self, - x: Union[Dict[str, torch.Tensor], torch.Tensor], - return_attn_weights: bool = False, - ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: - """ - Args: - x: Dict[str, Tensor] 每个 [B, d_model]，或已 stack 的 [B, n_tokens, d_model] - return_attn_weights: 若为 True 且策略为 attention，额外返回 attn_weights [B, n_tokens] - - Returns: - return_attn_weights=False: [B, fusion_dim] - return_attn_weights=True: ([B, fusion_dim], [B, n_tokens]) - """ - if isinstance(x, dict): - x = torch.stack(list(x.values()), dim=1) - - if self.strategy == "concat": - out = x.flatten(start_dim=1) - return (out, None) if return_attn_weights else out - - elif self.strategy == "avg": - out = x.mean(dim=1) - return (out, None) if return_attn_weights else out - - elif self.strategy == "max": - out = x.max(dim=1).values - return (out, None) if return_attn_weights else out - - elif self.strategy == "attention": - return self._attention_pooling(x, return_attn_weights) - - else: - raise ValueError(f"Unknown strategy: {self.strategy}") - - def _attention_pooling( - self, x: torch.Tensor, return_attn_weights: bool = False, - ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: - """ - Attention pooling: 用可学习 query 对 tokens 做加权求和 - - Args: - x: [B, n_tokens, d_model] - return_attn_weights: 是否返回权重 - - Returns: - return_attn_weights=False: [B, d_model] - return_attn_weights=True: ([B, d_model], [B, n_tokens]) - """ - B = x.size(0) - query = self.attn_query.expand(B, -1, -1) - - keys = self.attn_proj(x) - scores = torch.bmm(query, keys.transpose(1, 2)) / (self.d_model ** 0.5) - attn_weights = F.softmax(scores, dim=-1) # [B, 1, n_tokens] - - out = torch.bmm(attn_weights, x).squeeze(1) # [B, d_model] - - if return_attn_weights: - return out, attn_weights.squeeze(1) # [B, n_tokens] - return out +import torch +import torch.nn as nn +import torch.nn.functional as F +from typing import Dict, List, Literal, Optional, Tuple, Union + + +PoolingStrategy = Literal["concat", "avg", "max", "attention"] + + +class FusionLayer(nn.Module): + """ + 将多个 token 融合成单个向量。 + + 输入: Dict[str, Tensor] 或 [B, n_tokens, d_model] + 输出: [B, fusion_dim] + + 策略: + - concat: [B, n_tokens, d_model] -> [B, n_tokens * d_model] + - avg: [B, n_tokens, d_model] -> [B, d_model] + - max: [B, n_tokens, d_model] -> [B, d_model] + - attention: [B, n_tokens, d_model] -> [B, d_model] (learnable attention pooling) + """ + + def __init__( + self, + d_model: int, + n_tokens: int, + strategy: PoolingStrategy = "attention", + ) -> None: + """ + Args: + d_model: 每个 token 的维度 + n_tokens: token 数量（如 8） + strategy: 融合策略 + """ + super().__init__() + self.d_model = d_model + self.n_tokens = n_tokens + self.strategy = strategy + + if strategy == "concat": + self.fusion_dim = n_tokens * d_model + else: + self.fusion_dim = d_model + + # Attention pooling: learnable query + if strategy == "attention": + self.attn_query = nn.Parameter(torch.randn(1, 1, d_model)) + self.attn_proj = nn.Linear(d_model, d_model) + + def forward( + self, + x: Union[Dict[str, torch.Tensor], torch.Tensor], + return_attn_weights: bool = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """ + Args: + x: Dict[str, Tensor] 每个 [B, d_model]，或已 stack 的 [B, n_tokens, d_model] + return_attn_weights: 若为 True 且策略为 attention，额外返回 attn_weights [B, n_tokens] + + Returns: + return_attn_weights=False: [B, fusion_dim] + return_attn_weights=True: ([B, fusion_dim], [B, n_tokens]) + """ + if isinstance(x, dict): + x = torch.stack(list(x.values()), dim=1) + + if self.strategy == "concat": + out = x.flatten(start_dim=1) + return (out, None) if return_attn_weights else out + + elif self.strategy == "avg": + out = x.mean(dim=1) + return (out, None) if return_attn_weights else out + + elif self.strategy == "max": + out = x.max(dim=1).values + return (out, None) if return_attn_weights else out + + elif self.strategy == "attention": + return self._attention_pooling(x, return_attn_weights) + + else: + raise ValueError(f"Unknown strategy: {self.strategy}") + + def _attention_pooling( + self, x: torch.Tensor, return_attn_weights: bool = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + """ + Attention pooling: 用可学习 query 对 tokens 做加权求和 + + Args: + x: [B, n_tokens, d_model] + return_attn_weights: 是否返回权重 + + Returns: + return_attn_weights=False: [B, d_model] + return_attn_weights=True: ([B, d_model], [B, n_tokens]) + """ + B = x.size(0) + query = self.attn_query.expand(B, -1, -1) + + keys = self.attn_proj(x) + scores = torch.bmm(query, keys.transpose(1, 2)) / (self.d_model ** 0.5) + attn_weights = F.softmax(scores, dim=-1) # [B, 1, n_tokens] + + out = torch.bmm(attn_weights, x).squeeze(1) # [B, d_model] + + if return_attn_weights: + return out, attn_weights.squeeze(1) # [B, n_tokens] + return out + + +class ResidualConcatFusion(nn.Module): + """对真实 token 做 attention pooling，再用零初始化门把 MoE/LLM 旁路以残差方式加入。 + + g_moe / g_llm 初始为 0 → +moe/+llm 起点严格等于 baseline； + 旁路只有确实有用时才会被训练打开，从机制上保证“加了不会更差”。 + """ + + def __init__(self, d_model: int, strategy: PoolingStrategy = "attention") -> None: + super().__init__() + if strategy == "concat": + raise ValueError("ResidualConcatFusion 不支持 concat（token 数随开关变化）") + self.d_model = d_model + self.pool = FusionLayer(d_model=d_model, n_tokens=1, strategy=strategy) + self.fusion_dim = self.pool.fusion_dim + # 零初始化门控（可学习标量），旁路初始不参与 + self.g_moe = nn.Parameter(torch.zeros(())) + self.g_llm = nn.Parameter(torch.zeros(())) + + def forward( + self, + chem: torch.Tensor, + tab: torch.Tensor, + f_moe: Optional[torch.Tensor] = None, + f_llm: Optional[torch.Tensor] = None, + return_attn_weights: bool = False, + ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: + # 只对真实 token（chem + tab）做注意力池化，旁路不参与 softmax 竞争 + seq = torch.cat([chem, tab], dim=1) # [B, n_chem + n_cond, d_model] + pooled = self.pool(seq, return_attn_weights=return_attn_weights) + if return_attn_weights: + pooled, attn = pooled + + out = pooled + if f_moe is not None: + out = out + self.g_moe * f_moe # 残差 + 零初始化门 + if f_llm is not None: + out = out + self.g_llm * f_llm + + return (out, attn) if return_attn_weights else out \ No newline at end of file diff --git a/lnp_ml/modeling/layers/llm_prompt.py b/lnp_ml/modeling/layers/llm_prompt.py new file mode 100644 index 0000000..f9e01f4 --- /dev/null +++ b/lnp_ml/modeling/layers/llm_prompt.py @@ -0,0 +1,104 @@ +"""LLM 分子特征分支：用 MolT5 直接编码 SMILES 文本。""" + +import os +from typing import Dict, List, Optional + +import torch +import torch.nn as nn + + +# 权重默认路径，可用环境变量 MOLT5_PATH 覆盖 +DEFAULT_MOLT5_PATH = os.environ.get("MOLT5_PATH", "models/molt5-base") + + +class LLMPromptEncoder(nn.Module): + """用 MolT5 编码 SMILES 文本，输出分子特征 F_llm [B, d_model]。 + + - 冻结 encoder 时：对每个 SMILES 缓存其句向量，避免重复前向，降方差、提速。 + - use_lora=True 时：encoder 可训练（LoRA），不缓存。 + """ + + def __init__( + self, + d_model: int, + n_chem_tokens: int = 4, + n_cond_tokens: int = 4, + model_name_or_path: str = DEFAULT_MOLT5_PATH, + freeze: bool = True, + use_lora: bool = False, + lora_r: int = 8, + lora_alpha: int = 16, + lora_dropout: float = 0.05, + max_length: int = 128, + ) -> None: + super().__init__() + from transformers import AutoTokenizer, T5EncoderModel + + self.use_lora = use_lora + self.max_length = max_length + self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path) + self.encoder = T5EncoderModel.from_pretrained(model_name_or_path) + self.hidden_size = self.encoder.config.d_model + + if use_lora: + self._apply_lora(lora_r, lora_alpha, lora_dropout) + elif freeze: + for p in self.encoder.parameters(): + p.requires_grad = False + self._frozen = freeze and not use_lora + + self.proj_down = nn.Sequential( + nn.Linear(self.hidden_size, d_model), nn.LayerNorm(d_model) + ) + # 冻结特征缓存：smiles -> [H]（CPU） + self._cache: Dict[str, torch.Tensor] = {} + + def _apply_lora(self, r: int, alpha: int, dropout: float) -> None: + from peft import LoraConfig, get_peft_model + + for p in self.encoder.parameters(): + p.requires_grad = False + cfg = LoraConfig( + r=r, lora_alpha=alpha, lora_dropout=dropout, + target_modules=["q", "k", "v", "o"], bias="none", + ) + self.encoder = get_peft_model(self.encoder, cfg) + + def _mean_pool(self, last_hidden: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: + m = mask.unsqueeze(-1).float() # [B, L, 1] + return (last_hidden * m).sum(1) / m.sum(1).clamp(min=1e-6) + + @torch.no_grad() + def _encode_frozen(self, smiles: List[str], device: torch.device) -> torch.Tensor: + missing = [s for s in smiles if s not in self._cache] + if missing: + uniq = list(dict.fromkeys(missing)) + for i in range(0, len(uniq), 256): + chunk = uniq[i:i + 256] + enc = self.tokenizer( + chunk, padding=True, truncation=True, + max_length=self.max_length, return_tensors="pt", + ).to(device) + out = self.encoder(**enc).last_hidden_state + pooled = self._mean_pool(out, enc["attention_mask"]) + for s, v in zip(chunk, pooled): + self._cache[s] = v.cpu() + return torch.stack([self._cache[s] for s in smiles]).to(device) + + def _encode_trainable(self, smiles: List[str], device: torch.device) -> torch.Tensor: + enc = self.tokenizer( + list(smiles), padding=True, truncation=True, + max_length=self.max_length, return_tensors="pt", + ).to(device) + out = self.encoder(**enc).last_hidden_state + return self._mean_pool(out, enc["attention_mask"]) + + def forward(self, smiles: List[str], tab: Optional[torch.Tensor] = None) -> torch.Tensor: + """Args: smiles [B] SMILES 字符串列表。Returns: [B, d_model]。""" + device = self.proj_down[0].weight.device + feat = self._encode_frozen(smiles, device) if self._frozen \ + else self._encode_trainable(smiles, device) + return self.proj_down(feat) + + def clear_cache(self) -> None: + self._cache.clear() \ No newline at end of file diff --git a/lnp_ml/modeling/layers/set_transformer.py b/lnp_ml/modeling/layers/set_transformer.py new file mode 100644 index 0000000..b128bb4 --- /dev/null +++ b/lnp_ml/modeling/layers/set_transformer.py @@ -0,0 +1,123 @@ +"""Set Transformer 集合编码器。 +输入/输出形状均为 [B, n_tokens, d_model]， +n_tokens 可变（支持 3 或 4）。 +""" + +import torch +import torch.nn as nn + + +class MAB(nn.Module): + """多头注意力块：MAB(Q, K) = LN(H + FFN(H))，H = LN(Q + MultiHeadAttn(Q, K, K))。""" + + def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1, ln: bool = True) -> None: + super().__init__() + assert d_model % num_heads == 0, "d_model 必须能被 num_heads 整除" + self.attn = nn.MultiheadAttention( + d_model, num_heads, dropout=dropout, batch_first=True + ) + self.norm1 = nn.LayerNorm(d_model) if ln else nn.Identity() + self.norm2 = nn.LayerNorm(d_model) if ln else nn.Identity() + self.ffn = nn.Sequential( + nn.Linear(d_model, d_model * 4), + nn.GELU(), + nn.Dropout(dropout), + nn.Linear(d_model * 4, d_model), + nn.Dropout(dropout), + ) + + def forward(self, q: torch.Tensor, k: torch.Tensor) -> torch.Tensor: + """ + Args: + q: [B, n_q, d_model] + k: [B, n_k, d_model] + + Returns: + [B, n_q, d_model] + """ + attn_out, _ = self.attn(q, k, k) + h = self.norm1(q + attn_out) + return self.norm2(h + self.ffn(h)) + + +class SAB(nn.Module): + """集合自注意力块：SAB(X) = MAB(X, X)。""" + + def __init__(self, d_model: int, num_heads: int, dropout: float = 0.1, ln: bool = True) -> None: + super().__init__() + self.mab = MAB(d_model, num_heads, dropout, ln) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """[B, n, d_model] -> [B, n, d_model]""" + return self.mab(x, x) + + +class ISAB(nn.Module): + """诱导点集合注意力块：用 m 个可学习诱导点降低大集合的注意力复杂度。""" + + def __init__( + self, + d_model: int, + num_heads: int, + num_inducing: int = 16, + dropout: float = 0.1, + ln: bool = True, + ) -> None: + super().__init__() + # 可学习诱导点 + self.inducing = nn.Parameter(torch.empty(1, num_inducing, d_model)) + nn.init.xavier_uniform_(self.inducing) + self.mab_in = MAB(d_model, num_heads, dropout, ln) + self.mab_out = MAB(d_model, num_heads, dropout, ln) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """[B, n, d_model] -> [B, n, d_model]""" + inducing = self.inducing.expand(x.size(0), -1, -1) # [B, m, d_model] + h = self.mab_in(inducing, x) # [B, m, d_model] + return self.mab_out(x, h) # [B, n, d_model] + + +class SetTransformer(nn.Module): + """对化学 token 集合做 N 层集合自注意力，形状保持 [B, n_tokens, d_model]。""" + + def __init__( + self, + d_model: int, + num_heads: int = 8, + n_layers: int = 4, + dropout: float = 0.1, + block: str = "sab", + num_inducing: int = 16, + ln: bool = True, + ) -> None: + """ + Args: + d_model: token 维度 + num_heads: 注意力头数，d_head = d_model / num_heads + n_layers: 集合注意力层数 + dropout: dropout 比例 + block: 注意力块类型，"sab"（全自注意力）或 "isab"（诱导点） + num_inducing: ISAB 的诱导点数量（block="isab" 时生效） + ln: 是否使用 LayerNorm + """ + super().__init__() + self.block_type = block + + self.layers = nn.ModuleList() + for _ in range(n_layers): + if block == "isab": + self.layers.append(ISAB(d_model, num_heads, num_inducing, dropout, ln)) + else: + self.layers.append(SAB(d_model, num_heads, dropout, ln)) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + """ + Args: + x: [B, n_tokens, d_model] 化学 token 集合 + + Returns: + [B, n_tokens, d_model] 集合编码后的 token + """ + for layer in self.layers: + x = layer(x) + return x \ No newline at end of file diff --git a/lnp_ml/modeling/models.py b/lnp_ml/modeling/models.py index e680a52..188ef49 100644 --- a/lnp_ml/modeling/models.py +++ b/lnp_ml/modeling/models.py @@ -5,14 +5,21 @@ import torch.nn as nn from typing import Dict, List, Optional, Literal from lnp_ml.modeling.encoders import CachedRDKitEncoder, CachedMPNNEncoder -from lnp_ml.modeling.layers import TokenProjector, CrossModalAttention, FusionLayer, MoEBlock +from lnp_ml.modeling.layers import ( + TokenProjector, + SetTransformer, + ResidualConcatFusion, + MoEBlock, + LLMPromptEncoder, +) +from lnp_ml.modeling.layers.llm_prompt import DEFAULT_MOLT5_PATH from lnp_ml.modeling.heads import MultiTaskHead -PoolingStrategy = Literal["concat", "avg", "max", "attention"] +PoolingStrategy = Literal["attention", "avg", "max"] -# Token 维度配置（根据 ARCHITECTURE.md） +# Token 维度配置 DEFAULT_INPUT_DIMS = { # Channel A: 化学特征 "mpnn": 600, # D-MPNN embedding @@ -26,8 +33,21 @@ DEFAULT_INPUT_DIMS = { "exp": 32, # 实验条件 one-hot } -# Token 顺序（前 4 个为 Channel A，后 4 个为 Channel B） -TOKEN_ORDER = ["mpnn", "morgan", "maccs", "desc", "comp", "phys", "help", "exp"] +# 化学 / 配方 token 的键顺序 +CHEM_KEYS_WITH_MPNN = ["mpnn", "morgan", "maccs", "desc"] +CHEM_KEYS_NO_MPNN = ["morgan", "maccs", "desc"] +TAB_KEYS = ["comp", "phys", "help", "exp"] + +# backbone 权重前缀（用于预训练加载与导出） +BACKBONE_PREFIXES = ( + "token_projector.", + "set_transformer.", + "fusion.", + "moe.", + "llm_prompt.", +) +# 冻结的 MolT5 encoder 权重前缀，不纳入 backbone（由本地权重加载，不进 checkpoint） +LLM_FROZEN_PREFIX = "llm_prompt.encoder." class LNPModel(nn.Module): @@ -37,37 +57,47 @@ class LNPModel(nn.Module): 架构流程: 1. Encoders: SMILES -> 化学特征; tabular -> 配方/实验特征 2. TokenProjector: 统一到 d_model - 3. Stack: [B, 8, d_model] - 4. CrossModalAttention: Channel A (化学) <-> Channel B (配方/实验) - 5. FusionLayer: [B, 8, d_model] -> [B, fusion_dim] - 6. MultiTaskHead: 多任务预测 + 3. SetTransformer: 对化学 token 集合做置换等变编码 -> chem' + 4. MoE (可选): router 看 tab，expert 吃 chem' -> F_moe + 5. LLM (可选): chem'/tab 注入 MolT5 prompt -> F_llm + 6. ResidualConcatFusion: 拼接 chem'/tab/F_moe/F_llm -> attention pooling + 7. MultiTaskHead: 多任务预测 """ def __init__( self, # 模型维度 d_model: int = 256, - # Cross attention + # Set Transformer num_heads: int = 8, n_attn_layers: int = 4, + set_transformer_block: str = "sab", # Fusion fusion_strategy: PoolingStrategy = "attention", # Head head_hidden_dim: int = 128, # Dropout dropout: float = 0.1, - # MPNN encoder (可选，如果不用 MPNN 可以设为 None) + # MPNN encoder mpnn_checkpoint: Optional[str] = None, mpnn_ensemble_paths: Optional[List[str]] = None, mpnn_device: str = "cpu", # 输入维度配置 input_dims: Optional[Dict[str, int]] = None, - # ============ MoE 相关（新增） ============ + # ============ MoE 相关 ============ use_moe: bool = False, moe_n_experts: int = 4, moe_top_k: int = 2, moe_expert_hidden_mult: int = 2, moe_jitter_noise: float = 0.0, + # ============ LLM 相关 ============ + use_llm: bool = False, + llm_model_path: str = DEFAULT_MOLT5_PATH, + llm_freeze: bool = True, + llm_use_lora: bool = False, + llm_lora_r: int = 8, + llm_lora_alpha: int = 16, + llm_lora_dropout: float = 0.05, ) -> None: super().__init__() @@ -76,10 +106,7 @@ class LNPModel(nn.Module): self.use_mpnn = mpnn_checkpoint is not None or mpnn_ensemble_paths is not None # ============ Encoders ============ - # RDKit encoder (always used) self.rdkit_encoder = CachedRDKitEncoder() - - # MPNN encoder (optional) if self.use_mpnn: self.mpnn_encoder = CachedMPNNEncoder( checkpoint_path=mpnn_checkpoint, @@ -90,27 +117,28 @@ class LNPModel(nn.Module): self.mpnn_encoder = None # ============ Token Projector ============ - # 根据是否使用 MPNN 调整输入维度 proj_input_dims = {k: v for k, v in self.input_dims.items()} if not self.use_mpnn: proj_input_dims.pop("mpnn", None) - self.token_projector = TokenProjector( input_dims=proj_input_dims, d_model=d_model, dropout=dropout, ) - # ============ Cross Modal Attention ============ - n_tokens = 8 if self.use_mpnn else 7 - split_idx = 4 if self.use_mpnn else 3 # Channel A 的 token 数量 + # token 顺序与化学侧 token 数 + self.chem_keys = CHEM_KEYS_WITH_MPNN if self.use_mpnn else CHEM_KEYS_NO_MPNN + self.tab_keys = TAB_KEYS + self.token_order = self.chem_keys + self.tab_keys + self.split_idx = len(self.chem_keys) - self.cross_attention = CrossModalAttention( + # ============ Set Transformer ============ + self.set_transformer = SetTransformer( d_model=d_model, num_heads=num_heads, n_layers=n_attn_layers, - split_idx=split_idx, dropout=dropout, + block=set_transformer_block, ) # ============ MoE Block (可选) ============ @@ -119,24 +147,35 @@ class LNPModel(nn.Module): if use_moe: self.moe = MoEBlock( d_model=d_model, - n_chem_tokens=split_idx, + n_chem_tokens=self.split_idx, n_experts=moe_n_experts, top_k=moe_top_k, expert_hidden_mult=moe_expert_hidden_mult, dropout=dropout, jitter_noise=moe_jitter_noise, ) - n_fusion_tokens = n_tokens + 1 # 多一个 F_moe token else: self.moe = None - n_fusion_tokens = n_tokens - # ============ Fusion Layer ============ - self.fusion = FusionLayer( - d_model=d_model, - n_tokens=n_fusion_tokens, - strategy=fusion_strategy, - ) + # ============ LLM Prompt (可选) ============ + self.use_llm = use_llm + if use_llm: + self.llm_prompt = LLMPromptEncoder( + d_model=d_model, + n_chem_tokens=self.split_idx, + n_cond_tokens=len(self.tab_keys), + model_name_or_path=llm_model_path, + freeze=llm_freeze, + use_lora=llm_use_lora, + lora_r=llm_lora_r, + lora_alpha=llm_lora_alpha, + lora_dropout=llm_lora_dropout, + ) + else: + self.llm_prompt = None + + # ============ Residual Concat + Fusion ============ + self.fusion = ResidualConcatFusion(d_model=d_model, strategy=fusion_strategy) # ============ Multi-Task Head ============ self.head = MultiTaskHead( @@ -151,70 +190,51 @@ class LNPModel(nn.Module): tabular: Dict[str, torch.Tensor], ) -> torch.Tensor: """ - 内部方法：编码 SMILES 和 tabular，返回 stacked tokens。 - + 编码 SMILES 和 tabular，返回 stacked tokens。 + Returns: - stacked: [B, n_tokens, d_model] + stacked: [B, n_tokens, d_model]，顺序为 chem 在前、tab 在后 """ - # 获取目标设备（从 tabular 数据推断） device = tabular["comp"].device - - # 1. Encode SMILES - rdkit_features = self.rdkit_encoder(smiles) # {"morgan", "maccs", "desc"} - # 2. 合并所有特征 + rdkit_features = self.rdkit_encoder(smiles) + all_features: Dict[str, torch.Tensor] = {} - - # MPNN 特征（如果启用） if self.use_mpnn: mpnn_features = self.mpnn_encoder(smiles) all_features["mpnn"] = mpnn_features["mpnn"].to(device) - - # RDKit 特征（移到正确设备） all_features["morgan"] = rdkit_features["morgan"].to(device) all_features["maccs"] = rdkit_features["maccs"].to(device) all_features["desc"] = rdkit_features["desc"].to(device) - - # Tabular 特征（已在正确设备上） all_features["comp"] = tabular["comp"] all_features["phys"] = tabular["phys"] all_features["help"] = tabular["help"] all_features["exp"] = tabular["exp"] - # 3. Token Projector: 统一维度 - projected = self.token_projector(all_features) # Dict[str, [B, d_model]] - - # 4. Stack tokens: [B, n_tokens, d_model] - if self.use_mpnn: - token_order = ["mpnn", "morgan", "maccs", "desc", "comp", "phys", "help", "exp"] - else: - token_order = ["morgan", "maccs", "desc", "comp", "phys", "help", "exp"] - - stacked = torch.stack([projected[k] for k in token_order], dim=1) + projected = self.token_projector(all_features) + stacked = torch.stack([projected[k] for k in self.token_order], dim=1) return stacked - def _attended_with_moe(self, stacked: torch.Tensor) -> torch.Tensor: - """ - Cross-attention + 可选 MoE 旁路 → fusion 输入序列。 + def _backbone_from_stacked( + self, stacked: torch.Tensor, smiles: Optional[List[str]] = None + ) -> torch.Tensor: + chem = stacked[:, : self.split_idx, :] + tab = stacked[:, self.split_idx :, :] - - 不启用 MoE 时：返回 [B, n_tokens, d]，与原行为一致。 - - 启用 MoE 时：在最后追加一个 F_moe token，返回 [B, n_tokens + 1, d]。 + chem = self.set_transformer(chem) # chem' - 副作用: - 把本次 forward 的 MoE 副产物（aux loss / gates / probs）写到 - self._last_moe_extras，trainer 端通过 get_last_moe_extras() 读取。 - """ - attended = self.cross_attention(stacked) + f_moe = None if self.moe is not None: - split = self.cross_attention.split_idx - chem_prime = attended[:, :split, :] - tab_prime = attended[:, split:, :] - F_moe, extras = self.moe(chem_prime, tab_prime) + f_moe, extras = self.moe(chem, tab) self._last_moe_extras = extras - attended = torch.cat([attended, F_moe.unsqueeze(1)], dim=1) else: self._last_moe_extras = None - return attended + + f_llm = None + if self.llm_prompt is not None and smiles is not None: + f_llm = self.llm_prompt(smiles) + + return self.fusion(chem, tab, f_moe=f_moe, f_llm=f_llm) def forward_from_projected( self, @@ -225,15 +245,13 @@ class LNPModel(nn.Module): 从已投影的 stacked tokens 开始 forward，用于 Captum 归因。 Args: - stacked: [B, n_tokens, d_model] TokenProjector 输出后 stack 的张量。 - task: 指定单任务名 ("size", "pdi", "ee", "delivery", "biodist", "toxic")。 - 若为 None，返回 delivery head 的标量输出。 + stacked: [B, n_tokens, d_model] + task: 单任务名；None 时返回 delivery head 输出。 Returns: - [B, 1] 或 [B, num_classes] 对应任务的预测输出。 + 对应任务的预测输出。 """ - attended = self._attended_with_moe(stacked) - fused = self.fusion(attended) + fused = self._backbone_from_stacked(stacked) if task is None: task = "delivery" @@ -259,19 +277,10 @@ class LNPModel(nn.Module): 用原始特征替换 base_projected 中指定 token 的投影，然后 forward。 用于对单个 token 内部特征做 Captum 归因（如 desc 的 210 维）。 - - Args: - raw_feature: [B, input_dim] 某个 token 的原始特征 - feature_key: token 名称，如 "desc" - base_projected: [B, n_tokens, d_model] 其他 token 已投影好的张量 - task: 任务名 - - Returns: - 对应任务的预测输出 """ projected = self.token_projector.projectors[feature_key](raw_feature) gate = torch.sigmoid(self.token_projector.weights[feature_key]) - projected = projected * gate # [B, d_model] + projected = projected * gate token_order = list(self.token_projector.keys) token_idx = token_order.index(feature_key) @@ -286,38 +295,16 @@ class LNPModel(nn.Module): smiles: List[str], tabular: Dict[str, torch.Tensor], ) -> torch.Tensor: - """ - Backbone forward：编码 -> 投影 -> 注意力 -> (可选 MoE) -> 融合，不经过任务头。 - - 用于 pretrain 阶段或需要提取特征的场景。 - - Args: - smiles: SMILES 字符串列表，长度为 B - tabular: Dict[str, Tensor] - - Returns: - fused: [B, fusion_dim] 融合后的特征向量 - """ + """Backbone forward：编码 -> 投影 -> set transformer -> (MoE/LLM) -> 融合。""" stacked = self._encode_and_project(smiles, tabular) - attended = self._attended_with_moe(stacked) - fused = self.fusion(attended) - return fused + return self._backbone_from_stacked(stacked, smiles=smiles) def forward_delivery( self, smiles: List[str], tabular: Dict[str, torch.Tensor], ) -> torch.Tensor: - """ - 仅预测 delivery（用于 pretrain）。 - - Args: - smiles: SMILES 字符串列表，长度为 B - tabular: Dict[str, Tensor] - - Returns: - delivery: [B, 1] 预测的 delivery 值 - """ + """仅预测 delivery（用于 pretrain）。返回 [B, 1]。""" fused = self.forward_backbone(smiles, tabular) return self.head.delivery_head(fused) @@ -328,53 +315,34 @@ class LNPModel(nn.Module): ) -> Dict[str, torch.Tensor]: """ 完整的多任务 forward。 - - Args: - smiles: SMILES 字符串列表，长度为 B - tabular: Dict[str, Tensor]，包含: - - "comp": [B, 5] 配方比例 - - "phys": [B, 12] 物理参数 - - "help": [B, 4] Helper lipid - - "exp": [B, 32] 实验条件 Returns: - Dict[str, Tensor]: - - "size": [B, 1] - - "pdi": [B, 4] - - "ee": [B, 3] - - "delivery": [B, 1] - - "biodist": [B, 7] - - "toxic": [B, 2] + Dict[str, Tensor]: size [B,1], pdi [B,4], ee [B,3], + delivery [B,1], biodist [B,7], toxic [B,2] """ fused = self.forward_backbone(smiles, tabular) - outputs = self.head(fused) - return outputs + return self.head(fused) def clear_cache(self) -> None: """清空所有 encoder 的缓存""" self.rdkit_encoder.clear_cache() if self.mpnn_encoder is not None: self.mpnn_encoder.clear_cache() + if self.llm_prompt is not None and hasattr(self.llm_prompt, "clear_cache"): + self.llm_prompt.clear_cache() def get_last_moe_extras(self) -> Optional[Dict[str, torch.Tensor]]: - """返回最近一次 forward 中 MoE 模块的副产物（aux loss、gates、probs）。 - - 若未启用 MoE 或还未调用过 forward，返回 None。 - """ + """返回最近一次 forward 中 MoE 模块的副产物（aux loss、gates、probs）。""" return self._last_moe_extras def get_backbone_state_dict(self) -> Dict[str, torch.Tensor]: """ - 获取 backbone 部分的 state_dict（不含任务头）。 - - 包含: token_projector, cross_attention, fusion，以及（启用时）moe。 + 获取 backbone 部分的 state_dict（不含任务头，且排除冻结的 MolT5 encoder）。 """ - backbone_prefixes = ("token_projector.", "cross_attention.", "fusion.", "moe.") - backbone_keys = [ - name for name in self.state_dict().keys() - if name.startswith(backbone_prefixes) - ] - return {k: v for k, v in self.state_dict().items() if k in backbone_keys} + return { + k: v for k, v in self.state_dict().items() + if k.startswith(BACKBONE_PREFIXES) and not k.startswith(LLM_FROZEN_PREFIX) + } def get_delivery_head_state_dict(self) -> Dict[str, torch.Tensor]: """获取 delivery head 的 state_dict""" @@ -392,16 +360,11 @@ class LNPModel(nn.Module): """ 从预训练 checkpoint 加载 backbone 和（可选）delivery head 权重。 - Args: - pretrain_state_dict: 预训练模型的 state_dict - load_delivery_head: 是否加载 delivery head 权重 - strict: 是否严格匹配（默认 False，允许缺失/多余的键） + 冻结的 MolT5 encoder 权重不在加载范围内。 """ - backbone_prefixes = ("token_projector.", "cross_attention.", "fusion.", "moe.") - keys_to_load = [] for name in pretrain_state_dict.keys(): - if name.startswith(backbone_prefixes): + if name.startswith(BACKBONE_PREFIXES) and not name.startswith(LLM_FROZEN_PREFIX): keys_to_load.append(name) elif load_delivery_head and name.startswith("head.delivery_head."): keys_to_load.append(name) @@ -410,45 +373,49 @@ class LNPModel(nn.Module): k: v for k, v in pretrain_state_dict.items() if k in keys_to_load } - missing, unexpected = [], [] + unexpected = [] model_state = self.state_dict() for k, v in filtered_state_dict.items(): - if k in model_state: - if model_state[k].shape == v.shape: - model_state[k] = v - else: - unexpected.append( - f"{k} (shape mismatch: {model_state[k].shape} vs {v.shape})" - ) + if k in model_state and model_state[k].shape == v.shape: + model_state[k] = v else: unexpected.append(k) + self.load_state_dict(model_state, strict=False) - if strict and (missing or unexpected): - raise RuntimeError(f"Missing keys: {missing}, Unexpected keys: {unexpected}") + if strict and unexpected: + raise RuntimeError(f"Unexpected keys: {unexpected}") class LNPModelWithoutMPNN(LNPModel): - """不使用 MPNN 的简化版本""" + """不使用 MPNN 的简化版本（化学 token 为 3 个）""" def __init__( self, d_model: int = 256, num_heads: int = 8, n_attn_layers: int = 4, + set_transformer_block: str = "sab", fusion_strategy: PoolingStrategy = "attention", head_hidden_dim: int = 128, dropout: float = 0.1, input_dims: Optional[Dict[str, int]] = None, - # ============ MoE 相关（新增） ============ + # ============ MoE 相关 ============ use_moe: bool = False, moe_n_experts: int = 4, moe_top_k: int = 2, moe_expert_hidden_mult: int = 2, moe_jitter_noise: float = 0.0, + # ============ LLM 相关 ============ + use_llm: bool = False, + llm_model_path: str = DEFAULT_MOLT5_PATH, + llm_freeze: bool = True, + llm_use_lora: bool = False, + llm_lora_r: int = 8, + llm_lora_alpha: int = 16, + llm_lora_dropout: float = 0.05, ) -> None: - # 移除 mpnn 维度 dims = input_dims or DEFAULT_INPUT_DIMS.copy() dims.pop("mpnn", None) @@ -456,6 +423,7 @@ class LNPModelWithoutMPNN(LNPModel): d_model=d_model, num_heads=num_heads, n_attn_layers=n_attn_layers, + set_transformer_block=set_transformer_block, fusion_strategy=fusion_strategy, head_hidden_dim=head_hidden_dim, dropout=dropout, @@ -467,5 +435,11 @@ class LNPModelWithoutMPNN(LNPModel): moe_top_k=moe_top_k, moe_expert_hidden_mult=moe_expert_hidden_mult, moe_jitter_noise=moe_jitter_noise, - ) - + use_llm=use_llm, + llm_model_path=llm_model_path, + llm_freeze=llm_freeze, + llm_use_lora=llm_use_lora, + llm_lora_r=llm_lora_r, + llm_lora_alpha=llm_lora_alpha, + llm_lora_dropout=llm_lora_dropout, + ) \ No newline at end of file diff --git a/lnp_ml/modeling/nested_cv_optuna.py b/lnp_ml/modeling/nested_cv_optuna.py index 0fc546a..fb805de 100644 --- a/lnp_ml/modeling/nested_cv_optuna.py +++ b/lnp_ml/modeling/nested_cv_optuna.py @@ -1,933 +1,1021 @@ -""" -嵌套交叉验证 + Optuna 超参调优 - -外层 5-fold StratifiedKFold（20% test / 80% train） -内层 3-fold StratifiedKFold（在 80% 上做 Optuna 超参搜索） - -使用方法: - python -m lnp_ml.modeling.nested_cv_optuna - -或通过 Makefile: - make nested_cv_tune DEVICE=cuda -""" - -import json -import math -from datetime import datetime -from pathlib import Path -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np -import pandas as pd -import torch -from torch.utils.data import DataLoader, Subset -from sklearn.model_selection import StratifiedKFold -from loguru import logger -import typer - -try: - import optuna - from optuna.samplers import TPESampler -except ImportError: - optuna = None - TPESampler = None - -from lnp_ml.config import MODELS_DIR, INTERIM_DATA_DIR -from lnp_ml.dataset import ( - LNPDataset, - collate_fn, - process_dataframe, - TARGET_CLASSIFICATION_PDI, - TARGET_CLASSIFICATION_EE, - TARGET_TOXIC, -) -from tqdm import tqdm - -from lnp_ml.modeling.models import LNPModel, LNPModelWithoutMPNN -from lnp_ml.modeling.encoders.rdkit_encoder import CachedRDKitEncoder -from lnp_ml.modeling.trainer_balanced import ( - ClassWeights, - LossWeightsBalanced, - compute_class_weights_from_loader, - train_with_early_stopping, - train_fixed_epochs, - validate_balanced, -) - -# MPNN ensemble 默认路径 -DEFAULT_MPNN_ENSEMBLE_DIR = MODELS_DIR / "mpnn" / "all_amine_split_for_LiON" - -app = typer.Typer() - - -# ============ CompositeStrata 复合分层标签 ============ - -def build_composite_strata( - df: pd.DataFrame, - min_stratum_count: int = 5, -) -> Tuple[np.ndarray, Dict]: - """ - 构建复合分层标签（toxic × PDI × EE）。 - - Args: - df: 处理后的 DataFrame - min_stratum_count: 每个 stratum 最少样本数，低于此值合并为 RARE - - Returns: - (strata_array, strata_info) - - strata_array: 每个样本的 stratum 编码（整数） - - strata_info: 统计信息 - """ - n = len(df) - strata_labels = [] - - for i in range(n): - # Toxic stratum - if TARGET_TOXIC in df.columns: - toxic_val = df[TARGET_TOXIC].iloc[i] - if pd.notna(toxic_val) and toxic_val >= 0: - toxic_str = str(int(toxic_val)) - else: - toxic_str = "NA" - else: - toxic_str = "NA" - - # PDI stratum - if all(col in df.columns for col in TARGET_CLASSIFICATION_PDI): - pdi_vals = df[TARGET_CLASSIFICATION_PDI].iloc[i].values - if pdi_vals.sum() > 0: - pdi_str = str(int(np.argmax(pdi_vals))) - else: - pdi_str = "NA" - else: - pdi_str = "NA" - - # EE stratum - if all(col in df.columns for col in TARGET_CLASSIFICATION_EE): - ee_vals = df[TARGET_CLASSIFICATION_EE].iloc[i].values - if ee_vals.sum() > 0: - ee_str = str(int(np.argmax(ee_vals))) - else: - ee_str = "NA" - else: - ee_str = "NA" - - strata_labels.append(f"T{toxic_str}|P{pdi_str}|E{ee_str}") - - # 统计各 stratum 的样本数 - unique_strata, counts = np.unique(strata_labels, return_counts=True) - strata_counts = dict(zip(unique_strata, counts)) - - # 将稀疏 strata 合并为 RARE - rare_strata = [s for s, c in strata_counts.items() if c < min_stratum_count] - - final_labels = [] - for label in strata_labels: - if label in rare_strata: - final_labels.append("RARE") - else: - final_labels.append(label) - - # 编码为整数 - unique_final, encoded = np.unique(final_labels, return_inverse=True) - - strata_info = { - "original_strata_counts": strata_counts, - "rare_strata": rare_strata, - "final_strata": list(unique_final), - "final_strata_counts": dict(zip(*np.unique(final_labels, return_counts=True))), - "n_rare_merged": sum(strata_counts[s] for s in rare_strata) if rare_strata else 0, - } - - logger.info(f"Built composite strata: {len(unique_final)} unique strata") - logger.info(f" Rare strata merged: {len(rare_strata)} types, {strata_info['n_rare_merged']} samples") - - return encoded.astype(np.int64), strata_info - - -# ============ RDKit 缓存预热 ============ - -def warmup_rdkit_cache(smiles_list: List[str], batch_size: int = 256) -> Dict: - """预热 RDKit 特征缓存，返回可跨模型共享的缓存字典。""" - encoder = CachedRDKitEncoder() - unique_smiles = list(set(smiles_list)) - logger.info(f"Warming up RDKit cache for {len(unique_smiles)} unique SMILES...") - for i in tqdm(range(0, len(unique_smiles), batch_size), desc="Cache warmup"): - batch = unique_smiles[i:i + batch_size] - encoder(batch) - logger.success(f"Cache warmup complete. Cached {len(encoder._cache)} SMILES.") - return encoder._cache - - -# ============ 模型创建 ============ - -def find_mpnn_ensemble_paths(base_dir: Path = DEFAULT_MPNN_ENSEMBLE_DIR) -> List[str]: - """自动查找 MPNN ensemble 的 model.pt 文件。""" - model_paths = sorted(base_dir.glob("cv_*/fold_*/model_*/model.pt")) - if not model_paths: - raise FileNotFoundError(f"No model.pt files found in {base_dir}") - return [str(p) for p in model_paths] - - -def create_model( - d_model: int = 256, - num_heads: int = 8, - n_attn_layers: int = 4, - fusion_strategy: str = "attention", - head_hidden_dim: int = 128, - dropout: float = 0.1, - use_mpnn: bool = False, - mpnn_device: str = "cpu", - use_moe: bool = False, - moe_n_experts: int = 4, - moe_top_k: int = 2, - moe_expert_hidden_mult: int = 2, - moe_jitter_noise: float = 0.0, -) -> Union[LNPModel, LNPModelWithoutMPNN]: - """创建模型""" - moe_kwargs = dict( - use_moe=use_moe, - moe_n_experts=moe_n_experts, - moe_top_k=moe_top_k, - moe_expert_hidden_mult=moe_expert_hidden_mult, - moe_jitter_noise=moe_jitter_noise, - ) - - if use_mpnn: - ensemble_paths = find_mpnn_ensemble_paths() - return LNPModel( - d_model=d_model, - num_heads=num_heads, - n_attn_layers=n_attn_layers, - fusion_strategy=fusion_strategy, - head_hidden_dim=head_hidden_dim, - dropout=dropout, - mpnn_ensemble_paths=ensemble_paths, - mpnn_device=mpnn_device, - **moe_kwargs, - ) - else: - return LNPModelWithoutMPNN( - d_model=d_model, - num_heads=num_heads, - n_attn_layers=n_attn_layers, - fusion_strategy=fusion_strategy, - head_hidden_dim=head_hidden_dim, - dropout=dropout, - **moe_kwargs, - ) - - -# ============ 评估指标 ============ - -def evaluate_on_test( - model: torch.nn.Module, - test_loader: DataLoader, - device: torch.device, -) -> Dict: - """在测试集上评估模型""" - from scipy.special import rel_entr - from sklearn.metrics import ( - mean_squared_error, - mean_absolute_error, - r2_score, - accuracy_score, - precision_score, - recall_score, - f1_score, - ) - - model.eval() - - preds = { - "size": [], "delivery": [], "pdi": [], "ee": [], "toxic": [], "biodist": [] - } - targets = { - "size": [], "delivery": [], "pdi": [], "ee": [], "toxic": [], "biodist": [] - } - - with torch.no_grad(): - for batch in test_loader: - smiles = batch["smiles"] - tabular = {k: v.to(device) for k, v in batch["tabular"].items()} - tgts = batch["targets"] - masks = batch["mask"] - - outputs = model(smiles, tabular) - - # 收集预测和真实值 - for task in ["size", "delivery"]: - if task in masks and masks[task].any(): - m = masks[task] - key = task if task == "size" else "delivery" - preds[task].extend(outputs[key].squeeze(-1)[m].cpu().numpy().tolist()) - targets[task].extend(tgts[key][m].cpu().numpy().tolist()) - - for task in ["pdi", "ee", "toxic"]: - if task in masks and masks[task].any(): - m = masks[task] - preds[task].extend(outputs[task][m].argmax(dim=-1).cpu().numpy().tolist()) - targets[task].extend(tgts[task][m].cpu().numpy().tolist()) - - if "biodist" in masks and masks["biodist"].any(): - m = masks["biodist"] - preds["biodist"].extend(outputs["biodist"][m].cpu().numpy().tolist()) - targets["biodist"].extend(tgts["biodist"][m].cpu().numpy().tolist()) - - # 计算指标 - results = {} - - # 回归任务 - for task in ["size", "delivery"]: - if preds[task]: - p = np.array(preds[task]) - t = np.array(targets[task]) - results[task] = { - "n_samples": len(p), - "mse": float(mean_squared_error(t, p)), - "rmse": float(np.sqrt(mean_squared_error(t, p))), - "mae": float(mean_absolute_error(t, p)), - "r2": float(r2_score(t, p)), - } - - # 分类任务 - for task in ["pdi", "ee", "toxic"]: - if preds[task]: - p = np.array(preds[task]) - t = np.array(targets[task]) - results[task] = { - "n_samples": len(p), - "accuracy": float(accuracy_score(t, p)), - "precision": float(precision_score(t, p, average="macro", zero_division=0)), - "recall": float(recall_score(t, p, average="macro", zero_division=0)), - "f1": float(f1_score(t, p, average="macro", zero_division=0)), - } - - # 分布任务 - if preds["biodist"]: - p = np.array(preds["biodist"]) - t = np.array(targets["biodist"]) - - def kl_divergence(p_arr, q_arr, eps=1e-10): - p_arr = np.clip(p_arr, eps, 1.0) - q_arr = np.clip(q_arr, eps, 1.0) - return float(np.sum(rel_entr(p_arr, q_arr), axis=-1).mean()) - - def js_divergence(p_arr, q_arr, eps=1e-10): - p_arr = np.clip(p_arr, eps, 1.0) - q_arr = np.clip(q_arr, eps, 1.0) - m = 0.5 * (p_arr + q_arr) - return float(0.5 * (np.sum(rel_entr(p_arr, m), axis=-1) + np.sum(rel_entr(q_arr, m), axis=-1)).mean()) - - results["biodist"] = { - "n_samples": len(p), - "kl_divergence": kl_divergence(t, p), - "js_divergence": js_divergence(t, p), - } - - return results - - -# ============ 预训练权重加载 ============ - -def load_pretrain_weights_to_model( - model: Union[LNPModel, LNPModelWithoutMPNN], - pretrain_state_dict: Dict, - d_model: int, - pretrain_config: Dict, - load_delivery_head: bool = True, -) -> bool: - """ - 加载预训练权重到模型。 - - Returns: - 是否成功加载 - """ - if pretrain_config.get("d_model") != d_model: - logger.warning( - f"d_model mismatch: pretrain={pretrain_config.get('d_model')}, " - f"current={d_model}. Skipping pretrain loading." - ) - return False - - model.load_pretrain_weights( - pretrain_state_dict=pretrain_state_dict, - load_delivery_head=load_delivery_head, - strict=False, - ) - return True - - -# ============ 内层 Optuna 调参 ============ -def run_inner_optuna( - full_dataset: LNPDataset, - inner_train_indices: np.ndarray, - strata: np.ndarray, - device: torch.device, - n_trials: int = 20, - epochs_per_trial: int = 30, - patience: int = 10, - batch_size: int = 32, - n_inner_folds: int = 3, - use_mpnn: bool = False, - seed: int = 42, - study_path: Optional[Path] = None, - pretrain_state_dict: Optional[Dict] = None, - pretrain_config: Optional[Dict] = None, - load_delivery_head: bool = True, - rdkit_cache: Optional[Dict] = None, - use_moe: bool = False, - moe_n_experts: int = 4, - moe_top_k: int = 2, - moe_expert_hidden_mult: int = 2, - moe_jitter_noise: float = 0.0, -) -> Tuple[Dict, int, optuna.Study]: - """ - 在内层数据上运行 Optuna 超参搜索。 - - Args: - full_dataset: 完整数据集 - inner_train_indices: 内层训练数据的索引（相对于 full_dataset） - strata: 每个样本的分层标签 - device: 设备 - n_trials: Optuna 试验数 - epochs_per_trial: 每个试验的最大 epoch - patience: 早停耐心值 - batch_size: 批次大小 - n_inner_folds: 内层折数 - use_mpnn: 是否使用 MPNN - seed: 随机种子 - study_path: 可选的 study 持久化路径 - pretrain_state_dict: 预训练权重 - pretrain_config: 预训练配置 - load_delivery_head: 是否加载 delivery head 权重 - - Returns: - (best_params, epoch_mean, study) - """ - if optuna is None: - raise ImportError("Optuna not installed. Run: pip install optuna") - - inner_strata = strata[inner_train_indices] - - # 固定架构参数（与预训练一致，确保权重完整加载） - _cfg = pretrain_config or {} - fixed_d_model = _cfg.get("d_model", 256) - fixed_num_heads = _cfg.get("num_heads", 8) - fixed_n_attn_layers = _cfg.get("n_attn_layers", 4) - fixed_fusion_strategy = _cfg.get("fusion_strategy", "attention") - fixed_head_hidden_dim = _cfg.get("head_hidden_dim", 128) - logger.info( - f"Fixed architecture params: d_model={fixed_d_model}, num_heads={fixed_num_heads}, " - f"n_attn_layers={fixed_n_attn_layers}, fusion={fixed_fusion_strategy}, " - f"head_hidden_dim={fixed_head_hidden_dim}" - ) - - def objective(trial: optuna.Trial) -> float: - d_model = fixed_d_model - num_heads = fixed_num_heads - n_attn_layers = fixed_n_attn_layers - fusion_strategy = fixed_fusion_strategy - head_hidden_dim = fixed_head_hidden_dim - - # 搜索训练超参数 - dropout = trial.suggest_float("dropout", 0.1, 0.5) - lr = trial.suggest_float("lr", 1e-5, 1e-3, log=True) - weight_decay = trial.suggest_float("weight_decay", 1e-5, 1e-1, log=True) - backbone_lr_ratio = trial.suggest_float("backbone_lr_ratio", 0.01, 1.0, log=True) - - # 内层 3-fold CV - inner_cv = StratifiedKFold( - n_splits=n_inner_folds, shuffle=True, random_state=seed - ) - - fold_val_losses = [] - fold_best_epochs = [] - - for inner_fold, (inner_train_idx, inner_val_idx) in enumerate( - inner_cv.split(inner_train_indices, inner_strata) - ): - # 获取实际的数据集索引 - actual_train_idx = inner_train_indices[inner_train_idx] - actual_val_idx = inner_train_indices[inner_val_idx] - - # 创建 DataLoader - train_subset = Subset(full_dataset, actual_train_idx.tolist()) - val_subset = Subset(full_dataset, actual_val_idx.tolist()) - - train_loader = DataLoader( - train_subset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn - ) - val_loader = DataLoader( - val_subset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn - ) - - # 计算类权重 - class_weights = compute_class_weights_from_loader(train_loader) - - # 创建模型 - model = create_model( - d_model=d_model, - num_heads=num_heads, - n_attn_layers=n_attn_layers, - fusion_strategy=fusion_strategy, - head_hidden_dim=head_hidden_dim, - dropout=dropout, - use_mpnn=use_mpnn, - mpnn_device=device.type, - use_moe=use_moe, - moe_n_experts=moe_n_experts, - moe_top_k=moe_top_k, - moe_expert_hidden_mult=moe_expert_hidden_mult, - moe_jitter_noise=moe_jitter_noise, - ) - if rdkit_cache is not None: - model.rdkit_encoder._cache = rdkit_cache - - # 加载预训练权重 - if pretrain_state_dict is not None and pretrain_config is not None: - load_pretrain_weights_to_model( - model, pretrain_state_dict, d_model, pretrain_config, load_delivery_head - ) - - # 训练（带早停） - result = train_with_early_stopping( - model=model, - train_loader=train_loader, - val_loader=val_loader, - device=device, - lr=lr, - weight_decay=weight_decay, - epochs=epochs_per_trial, - patience=patience, - class_weights=class_weights, - backbone_lr_ratio=backbone_lr_ratio, - ) - - fold_val_losses.append(result["best_val_loss"]) - fold_best_epochs.append(result["best_epoch"]) - - # 记录 epoch_mean 到 trial - epoch_mean = int(round(np.mean(fold_best_epochs))) - trial.set_user_attr("epoch_mean", epoch_mean) - trial.set_user_attr("fold_best_epochs", fold_best_epochs) - - return np.mean(fold_val_losses) - - # 创建 study - storage = None - if study_path is not None: - storage = f"sqlite:///{study_path}" - - study = optuna.create_study( - direction="minimize", - sampler=TPESampler(seed=seed), - storage=storage, - study_name="inner_optuna", - load_if_exists=True, - ) - - study.optimize(objective, n_trials=n_trials, show_progress_bar=True) - - best_params = dict(study.best_trial.params) - best_params.update({ - "d_model": fixed_d_model, - "num_heads": fixed_num_heads, - "n_attn_layers": fixed_n_attn_layers, - "fusion_strategy": fixed_fusion_strategy, - "head_hidden_dim": fixed_head_hidden_dim, - }) - epoch_mean = study.best_trial.user_attrs.get("epoch_mean", epochs_per_trial) - - logger.info(f"Best trial: {study.best_trial.number}") - logger.info(f"Best val_loss: {study.best_trial.value:.4f}") - logger.info(f"Best params: {best_params}") - logger.info(f"Epoch mean: {epoch_mean}") - - return best_params, epoch_mean, study - - -# ============ 单 fold 执行（可跨进程调用） ============ -def _run_single_outer_fold( - outer_fold: int, - outer_train_idx: np.ndarray, - outer_test_idx: np.ndarray, - df: pd.DataFrame, - strata: np.ndarray, - fold_dir: Path, - n_trials: int, - epochs_per_trial: int, - inner_patience: int, - batch_size: int, - n_inner_folds: int, - use_mpnn: bool, - seed: int, - pretrain_state_dict: Optional[Dict], - pretrain_config: Optional[Dict], - load_delivery_head: bool, - device_str: str, - use_moe: bool = False, - moe_n_experts: int = 4, - moe_top_k: int = 2, - moe_expert_hidden_mult: int = 2, - moe_jitter_noise: float = 0.0, -) -> Dict: - """ - 执行单个外层 fold 的完整流程（内层调参 + 外层训练 + 评估）。 - - 所有参数均为可序列化类型，以支持 spawn 多进程。 - """ - device = torch.device(device_str) - fold_dir = Path(fold_dir) - fold_dir.mkdir(parents=True, exist_ok=True) - - full_dataset = LNPDataset(df) - - logger.info(f"\n{'='*60}") - logger.info(f"OUTER FOLD {outer_fold}") - logger.info(f"{'='*60}") - logger.info(f"Train: {len(outer_train_idx)}, Test: {len(outer_test_idx)}") - - # 预热 RDKit 缓存（在整个 fold 内共享） - rdkit_cache = warmup_rdkit_cache(full_dataset.smiles) - - # 保存 split indices - with open(fold_dir / "splits.json", "w") as f: - json.dump({ - "outer_train_idx": outer_train_idx.tolist(), - "outer_test_idx": outer_test_idx.tolist(), - }, f) - - # 内层 Optuna 调参 - logger.info(f"\nRunning inner Optuna with {n_trials} trials...") - study_path = fold_dir / "optuna_study.sqlite3" - - best_params, epoch_mean, study = run_inner_optuna( - full_dataset=full_dataset, - inner_train_indices=outer_train_idx, - strata=strata, - device=device, - n_trials=n_trials, - epochs_per_trial=epochs_per_trial, - patience=inner_patience, - batch_size=batch_size, - n_inner_folds=n_inner_folds, - use_mpnn=use_mpnn, - seed=seed + outer_fold, - study_path=study_path, - pretrain_state_dict=pretrain_state_dict, - pretrain_config=pretrain_config, - load_delivery_head=load_delivery_head, - rdkit_cache=rdkit_cache, - use_moe=use_moe, - moe_n_experts=moe_n_experts, - moe_top_k=moe_top_k, - moe_expert_hidden_mult=moe_expert_hidden_mult, - moe_jitter_noise=moe_jitter_noise, - ) - - # 保存最佳参数 - with open(fold_dir / "best_params.json", "w") as f: - json.dump(best_params, f, indent=2) - - with open(fold_dir / "epoch_mean.json", "w") as f: - json.dump({"epoch_mean": epoch_mean}, f) - - # 外层训练（使用最优超参，固定 epoch 数，不 early-stop） - logger.info(f"\nTraining outer fold with best params, epochs={epoch_mean}...") - - train_subset = Subset(full_dataset, outer_train_idx.tolist()) - test_subset = Subset(full_dataset, outer_test_idx.tolist()) - - train_loader = DataLoader( - train_subset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn - ) - test_loader = DataLoader( - test_subset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn - ) - - class_weights = compute_class_weights_from_loader(train_loader) - - model = create_model( - d_model=best_params["d_model"], - num_heads=best_params["num_heads"], - n_attn_layers=best_params["n_attn_layers"], - fusion_strategy=best_params["fusion_strategy"], - head_hidden_dim=best_params["head_hidden_dim"], - dropout=best_params["dropout"], - use_mpnn=use_mpnn, - mpnn_device=device.type, - use_moe=use_moe, - moe_n_experts=moe_n_experts, - moe_top_k=moe_top_k, - moe_expert_hidden_mult=moe_expert_hidden_mult, - moe_jitter_noise=moe_jitter_noise, - ) - model.rdkit_encoder._cache = rdkit_cache - - if pretrain_state_dict is not None and pretrain_config is not None: - loaded = load_pretrain_weights_to_model( - model, pretrain_state_dict, best_params["d_model"], - pretrain_config, load_delivery_head - ) - if loaded: - logger.info(f"Loaded pretrain weights for outer fold {outer_fold}") - - train_result = train_fixed_epochs( - model=model, - train_loader=train_loader, - val_loader=None, - device=device, - lr=best_params["lr"], - weight_decay=best_params["weight_decay"], - epochs=epoch_mean, - class_weights=class_weights, - use_cosine_annealing=True, - backbone_lr_ratio=best_params.get("backbone_lr_ratio", 1.0), - ) - - model.load_state_dict(train_result["final_state"]) - model = model.to(device) - - config = { - "d_model": best_params["d_model"], - "num_heads": best_params["num_heads"], - "n_attn_layers": best_params["n_attn_layers"], - "fusion_strategy": best_params["fusion_strategy"], - "head_hidden_dim": best_params["head_hidden_dim"], - "dropout": best_params["dropout"], - "use_mpnn": use_mpnn, - "use_moe": use_moe, - "moe_n_experts": moe_n_experts, - "moe_top_k": moe_top_k, - "moe_expert_hidden_mult": moe_expert_hidden_mult, - "moe_jitter_noise": moe_jitter_noise, - } - - torch.save({ - "model_state_dict": train_result["final_state"], - "config": config, - "epoch_mean": epoch_mean, - "best_params": best_params, - }, fold_dir / "model.pt") - - with open(fold_dir / "history.json", "w") as f: - json.dump(train_result["history"], f, indent=2) - - # 在测试集上评估 - logger.info("Evaluating on outer test set...") - test_metrics = evaluate_on_test(model, test_loader, device) - - with open(fold_dir / "test_metrics.json", "w") as f: - json.dump(test_metrics, f, indent=2) - - logger.info(f"\nOuter Fold {outer_fold} Test Results:") - for task, metrics in test_metrics.items(): - if "rmse" in metrics: - logger.info(f" {task}: RMSE={metrics['rmse']:.4f}, R²={metrics['r2']:.4f}") - elif "accuracy" in metrics: - logger.info(f" {task}: Acc={metrics['accuracy']:.4f}, F1={metrics['f1']:.4f}") - elif "kl_divergence" in metrics: - logger.info(f" {task}: KL={metrics['kl_divergence']:.4f}, JS={metrics['js_divergence']:.4f}") - - return { - "fold": outer_fold, - "best_params": best_params, - "epoch_mean": epoch_mean, - "test_metrics": test_metrics, - } - - -# ============ 主流程 ============ -@app.command() -def main( - input_path: Path = INTERIM_DATA_DIR / "internal.csv", - output_dir: Path = MODELS_DIR / "nested_cv", - # CV 参数 - n_outer_folds: int = 5, - n_inner_folds: int = 3, - min_stratum_count: int = 5, - seed: int = 42, - # Optuna 参数 - n_trials: int = 20, - epochs_per_trial: int = 30, - inner_patience: int = 10, - # 训练参数 - batch_size: int = 32, - # 预训练权重 - init_from_pretrain: Optional[Path] = None, - load_delivery_head: bool = False, - # MPNN - use_mpnn: bool = False, - # MoE（新增） - use_moe: bool = False, - moe_n_experts: int = 4, - moe_top_k: int = 2, - moe_expert_hidden_mult: int = 2, - moe_jitter_noise: float = 0.0, - # 并行 - parallel: bool = False, - # 设备 - device: str = "cuda" if torch.cuda.is_available() else "cpu", -): - """ - 嵌套交叉验证 + Optuna 超参调优。 - - 外层 5-fold（20% test / 80% train），内层 3-fold Optuna 调参。 - 外层训练不使用 early-stopping，epoch 数使用内层 best trial 的 epoch_mean。 - - 使用 --init-from-pretrain 从预训练 checkpoint 初始化模型权重。 - 使用 --parallel 同时运行所有外层 fold（需要足够 GPU 显存）。 - """ - if optuna is None: - logger.error("Optuna not installed. Run: pip install optuna") - raise typer.Exit(1) - - logger.info(f"Using device: {device}") - device = torch.device(device) - - # 加载预训练权重（如果指定） - pretrain_state_dict = None - pretrain_config = None - 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", 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')})") - else: - logger.warning(f"Pretrain checkpoint not found: {init_from_pretrain}, skipping") - - # 创建输出目录（带时间戳） - run_name = datetime.now().strftime("%Y%m%d_%H%M%S") - run_dir = output_dir / run_name - run_dir.mkdir(parents=True, exist_ok=True) - logger.info(f"Output directory: {run_dir}") - - # 加载数据 - logger.info(f"Loading data from {input_path}") - df = pd.read_csv(input_path) - logger.info(f"Loaded {len(df)} samples") - - # 处理数据 - logger.info("Processing dataframe...") - df = process_dataframe(df) - - # 构建复合分层标签 - logger.info("Building composite strata...") - strata, strata_info = build_composite_strata(df, min_stratum_count) - - # 保存 strata 信息 - with open(run_dir / "strata_info.json", "w") as f: - json.dump(strata_info, f, indent=2, default=str) - - # 创建完整数据集（仅用于获取样本数做 split） - n_samples = len(LNPDataset(df)) - - # 外层 CV split - outer_cv = StratifiedKFold( - n_splits=n_outer_folds, shuffle=True, random_state=seed - ) - - device_str = str(device) - fold_args = [] - for outer_fold, (outer_train_idx, outer_test_idx) in enumerate( - outer_cv.split(np.arange(n_samples), strata) - ): - fold_args.append(dict( - outer_fold=outer_fold, - outer_train_idx=outer_train_idx, - outer_test_idx=outer_test_idx, - df=df, - strata=strata, - fold_dir=run_dir / f"outer_fold_{outer_fold}", - n_trials=n_trials, - epochs_per_trial=epochs_per_trial, - inner_patience=inner_patience, - batch_size=batch_size, - n_inner_folds=n_inner_folds, - use_mpnn=use_mpnn, - seed=seed, - pretrain_state_dict=pretrain_state_dict, - pretrain_config=pretrain_config, - load_delivery_head=load_delivery_head, - device_str=device_str, - use_moe=use_moe, - moe_n_experts=moe_n_experts, - moe_top_k=moe_top_k, - moe_expert_hidden_mult=moe_expert_hidden_mult, - moe_jitter_noise=moe_jitter_noise, - )) - - if parallel: - import multiprocessing as mp - from concurrent.futures import ProcessPoolExecutor - - ctx = mp.get_context("spawn") - logger.info(f"Running {n_outer_folds} outer folds in PARALLEL (spawn)") - with ProcessPoolExecutor(max_workers=n_outer_folds, mp_context=ctx) as executor: - futures = [executor.submit(_run_single_outer_fold, **args) for args in fold_args] - outer_results = [f.result() for f in futures] - outer_results.sort(key=lambda r: r["fold"]) - else: - logger.info(f"Running {n_outer_folds} outer folds SEQUENTIALLY") - outer_results = [] - for args in fold_args: - result = _run_single_outer_fold(**args) - outer_results.append(result) - - # 汇总结果 - logger.info("\n" + "=" * 60) - logger.info("NESTED CV COMPLETE") - logger.info("=" * 60) - - # 计算汇总统计 - summary = {"fold_results": outer_results} - - # 对每个任务计算均值和标准差 - tasks_with_metrics = {} - for result in outer_results: - for task, metrics in result["test_metrics"].items(): - if task not in tasks_with_metrics: - tasks_with_metrics[task] = {k: [] for k in metrics.keys() if k != "n_samples"} - for k, v in metrics.items(): - if k != "n_samples": - tasks_with_metrics[task][k].append(v) - - summary["summary_stats"] = {} - for task, metrics_dict in tasks_with_metrics.items(): - summary["summary_stats"][task] = {} - for metric_name, values in metrics_dict.items(): - summary["summary_stats"][task][f"{metric_name}_mean"] = float(np.mean(values)) - summary["summary_stats"][task][f"{metric_name}_std"] = float(np.std(values)) - - # 打印汇总 - logger.info("\n[Summary Statistics]") - for task, stats in summary["summary_stats"].items(): - if "rmse_mean" in stats: - logger.info( - f" {task}: RMSE={stats['rmse_mean']:.4f}±{stats['rmse_std']:.4f}, " - f"R²={stats['r2_mean']:.4f}±{stats['r2_std']:.4f}" - ) - elif "accuracy_mean" in stats: - logger.info( - f" {task}: Acc={stats['accuracy_mean']:.4f}±{stats['accuracy_std']:.4f}, " - f"F1={stats['f1_mean']:.4f}±{stats['f1_std']:.4f}" - ) - elif "kl_divergence_mean" in stats: - logger.info( - f" {task}: KL={stats['kl_divergence_mean']:.4f}±{stats['kl_divergence_std']:.4f}, " - f"JS={stats['js_divergence_mean']:.4f}±{stats['js_divergence_std']:.4f}" - ) - - # 保存汇总 - with open(run_dir / "summary.json", "w") as f: - json.dump(summary, f, indent=2) - - logger.success(f"\nAll results saved to {run_dir}") - - -if __name__ == "__main__": - app() - +""" +嵌套交叉验证 + Optuna 超参调优 + +外层 5-fold StratifiedKFold（20% test / 80% train） +内层 3-fold StratifiedKFold（在 80% 上做 Optuna 超参搜索） + +使用方法: + python -m lnp_ml.modeling.nested_cv_optuna + +或通过 Makefile: + make nested_cv_tune DEVICE=cuda +""" + +import json +import math +from datetime import datetime +from pathlib import Path +from typing import Dict, List, Optional, Tuple, Union + +import numpy as np +import pandas as pd +import torch +from torch.utils.data import DataLoader, Subset +from sklearn.model_selection import StratifiedKFold +from loguru import logger +import typer + +try: + import optuna + from optuna.samplers import TPESampler +except ImportError: + optuna = None + TPESampler = None + +from lnp_ml.config import MODELS_DIR, INTERIM_DATA_DIR +from lnp_ml.dataset import ( + LNPDataset, + collate_fn, + process_dataframe, + TARGET_CLASSIFICATION_PDI, + TARGET_CLASSIFICATION_EE, + TARGET_TOXIC, +) +from tqdm import tqdm + +from lnp_ml.modeling.models import LNPModel, LNPModelWithoutMPNN +from lnp_ml.modeling.layers.llm_prompt import DEFAULT_MOLT5_PATH +from lnp_ml.utils.seed import set_global_seed +from lnp_ml.modeling.encoders.rdkit_encoder import CachedRDKitEncoder +from lnp_ml.modeling.trainer_balanced import ( + ClassWeights, + LossWeightsBalanced, + compute_class_weights_from_loader, + train_with_early_stopping, + train_fixed_epochs, + validate_balanced, +) + +# MPNN ensemble 默认路径 +DEFAULT_MPNN_ENSEMBLE_DIR = MODELS_DIR / "mpnn" / "all_amine_split_for_LiON" + +app = typer.Typer() + + +# ============ CompositeStrata 复合分层标签 ============ + +def build_composite_strata( + df: pd.DataFrame, + min_stratum_count: int = 5, +) -> Tuple[np.ndarray, Dict]: + """ + 构建复合分层标签（toxic × PDI × EE）。 + + Args: + df: 处理后的 DataFrame + min_stratum_count: 每个 stratum 最少样本数，低于此值合并为 RARE + + Returns: + (strata_array, strata_info) + - strata_array: 每个样本的 stratum 编码（整数） + - strata_info: 统计信息 + """ + n = len(df) + strata_labels = [] + + for i in range(n): + # Toxic stratum + if TARGET_TOXIC in df.columns: + toxic_val = df[TARGET_TOXIC].iloc[i] + if pd.notna(toxic_val) and toxic_val >= 0: + toxic_str = str(int(toxic_val)) + else: + toxic_str = "NA" + else: + toxic_str = "NA" + + # PDI stratum + if all(col in df.columns for col in TARGET_CLASSIFICATION_PDI): + pdi_vals = df[TARGET_CLASSIFICATION_PDI].iloc[i].values + if pdi_vals.sum() > 0: + pdi_str = str(int(np.argmax(pdi_vals))) + else: + pdi_str = "NA" + else: + pdi_str = "NA" + + # EE stratum + if all(col in df.columns for col in TARGET_CLASSIFICATION_EE): + ee_vals = df[TARGET_CLASSIFICATION_EE].iloc[i].values + if ee_vals.sum() > 0: + ee_str = str(int(np.argmax(ee_vals))) + else: + ee_str = "NA" + else: + ee_str = "NA" + + strata_labels.append(f"T{toxic_str}|P{pdi_str}|E{ee_str}") + + # 统计各 stratum 的样本数 + unique_strata, counts = np.unique(strata_labels, return_counts=True) + strata_counts = dict(zip(unique_strata, counts)) + + # 将稀疏 strata 合并为 RARE + rare_strata = [s for s, c in strata_counts.items() if c < min_stratum_count] + + final_labels = [] + for label in strata_labels: + if label in rare_strata: + final_labels.append("RARE") + else: + final_labels.append(label) + + # 编码为整数 + unique_final, encoded = np.unique(final_labels, return_inverse=True) + + strata_info = { + "original_strata_counts": strata_counts, + "rare_strata": rare_strata, + "final_strata": list(unique_final), + "final_strata_counts": dict(zip(*np.unique(final_labels, return_counts=True))), + "n_rare_merged": sum(strata_counts[s] for s in rare_strata) if rare_strata else 0, + } + + logger.info(f"Built composite strata: {len(unique_final)} unique strata") + logger.info(f" Rare strata merged: {len(rare_strata)} types, {strata_info['n_rare_merged']} samples") + + return encoded.astype(np.int64), strata_info + + +# ============ RDKit 缓存预热 ============ + +def warmup_rdkit_cache(smiles_list: List[str], batch_size: int = 256) -> Dict: + """预热 RDKit 特征缓存，返回可跨模型共享的缓存字典。""" + encoder = CachedRDKitEncoder() + unique_smiles = list(set(smiles_list)) + logger.info(f"Warming up RDKit cache for {len(unique_smiles)} unique SMILES...") + for i in tqdm(range(0, len(unique_smiles), batch_size), desc="Cache warmup"): + batch = unique_smiles[i:i + batch_size] + encoder(batch) + logger.success(f"Cache warmup complete. Cached {len(encoder._cache)} SMILES.") + return encoder._cache + + +# ============ 模型创建 ============ + +def find_mpnn_ensemble_paths(base_dir: Path = DEFAULT_MPNN_ENSEMBLE_DIR) -> List[str]: + """自动查找 MPNN ensemble 的 model.pt 文件。""" + model_paths = sorted(base_dir.glob("cv_*/fold_*/model_*/model.pt")) + if not model_paths: + raise FileNotFoundError(f"No model.pt files found in {base_dir}") + return [str(p) for p in model_paths] + + +def create_model( + d_model: int = 256, + num_heads: int = 8, + n_attn_layers: int = 4, + fusion_strategy: str = "attention", + head_hidden_dim: int = 128, + dropout: float = 0.1, + use_mpnn: bool = False, + mpnn_device: str = "cpu", + set_transformer_block: str = "sab", + # MoE + use_moe: bool = False, + moe_n_experts: int = 4, + moe_top_k: int = 2, + moe_expert_hidden_mult: int = 2, + moe_jitter_noise: float = 0.0, + # LLM（打包成 dict 以减少多进程参数传递） + llm_kwargs: Optional[Dict] = None, +) -> Union[LNPModel, LNPModelWithoutMPNN]: + """创建模型。llm_kwargs 含 use_llm / llm_model_path / llm_freeze / llm_use_lora / llm_lora_*。""" + extra_kwargs = dict( + set_transformer_block=set_transformer_block, + use_moe=use_moe, + moe_n_experts=moe_n_experts, + moe_top_k=moe_top_k, + moe_expert_hidden_mult=moe_expert_hidden_mult, + moe_jitter_noise=moe_jitter_noise, + **(llm_kwargs or {}), + ) + + if use_mpnn: + ensemble_paths = find_mpnn_ensemble_paths() + return LNPModel( + d_model=d_model, + num_heads=num_heads, + n_attn_layers=n_attn_layers, + fusion_strategy=fusion_strategy, + head_hidden_dim=head_hidden_dim, + dropout=dropout, + mpnn_ensemble_paths=ensemble_paths, + mpnn_device=mpnn_device, + **extra_kwargs, + ) + else: + return LNPModelWithoutMPNN( + d_model=d_model, + num_heads=num_heads, + n_attn_layers=n_attn_layers, + fusion_strategy=fusion_strategy, + head_hidden_dim=head_hidden_dim, + dropout=dropout, + **extra_kwargs, + ) + + +# ============ 评估指标 ============ + +def evaluate_on_test( + model: torch.nn.Module, + test_loader: DataLoader, + device: torch.device, +) -> Dict: + """在测试集上评估模型""" + from scipy.special import rel_entr + from sklearn.metrics import ( + mean_squared_error, + mean_absolute_error, + r2_score, + accuracy_score, + precision_score, + recall_score, + f1_score, + ) + + model.eval() + + preds = { + "size": [], "delivery": [], "pdi": [], "ee": [], "toxic": [], "biodist": [] + } + targets = { + "size": [], "delivery": [], "pdi": [], "ee": [], "toxic": [], "biodist": [] + } + + with torch.no_grad(): + for batch in test_loader: + smiles = batch["smiles"] + tabular = {k: v.to(device) for k, v in batch["tabular"].items()} + tgts = batch["targets"] + masks = batch["mask"] + + outputs = model(smiles, tabular) + + # 收集预测和真实值 + for task in ["size", "delivery"]: + if task in masks and masks[task].any(): + m = masks[task] + key = task if task == "size" else "delivery" + preds[task].extend(outputs[key].squeeze(-1)[m].cpu().numpy().tolist()) + targets[task].extend(tgts[key][m].cpu().numpy().tolist()) + + for task in ["pdi", "ee", "toxic"]: + if task in masks and masks[task].any(): + m = masks[task] + preds[task].extend(outputs[task][m].argmax(dim=-1).cpu().numpy().tolist()) + targets[task].extend(tgts[task][m].cpu().numpy().tolist()) + + if "biodist" in masks and masks["biodist"].any(): + m = masks["biodist"] + preds["biodist"].extend(outputs["biodist"][m].cpu().numpy().tolist()) + targets["biodist"].extend(tgts["biodist"][m].cpu().numpy().tolist()) + + # 计算指标 + results = {} + + # 回归任务 + for task in ["size", "delivery"]: + if preds[task]: + p = np.array(preds[task]) + t = np.array(targets[task]) + results[task] = { + "n_samples": len(p), + "mse": float(mean_squared_error(t, p)), + "rmse": float(np.sqrt(mean_squared_error(t, p))), + "mae": float(mean_absolute_error(t, p)), + "r2": float(r2_score(t, p)), + } + + # 分类任务 + for task in ["pdi", "ee", "toxic"]: + if preds[task]: + p = np.array(preds[task]) + t = np.array(targets[task]) + results[task] = { + "n_samples": len(p), + "accuracy": float(accuracy_score(t, p)), + "precision": float(precision_score(t, p, average="macro", zero_division=0)), + "recall": float(recall_score(t, p, average="macro", zero_division=0)), + "f1": float(f1_score(t, p, average="macro", zero_division=0)), + } + + # 分布任务 + if preds["biodist"]: + p = np.array(preds["biodist"]) + t = np.array(targets["biodist"]) + + def kl_divergence(p_arr, q_arr, eps=1e-10): + p_arr = np.clip(p_arr, eps, 1.0) + q_arr = np.clip(q_arr, eps, 1.0) + return float(np.sum(rel_entr(p_arr, q_arr), axis=-1).mean()) + + def js_divergence(p_arr, q_arr, eps=1e-10): + p_arr = np.clip(p_arr, eps, 1.0) + q_arr = np.clip(q_arr, eps, 1.0) + m = 0.5 * (p_arr + q_arr) + return float(0.5 * (np.sum(rel_entr(p_arr, m), axis=-1) + np.sum(rel_entr(q_arr, m), axis=-1)).mean()) + + results["biodist"] = { + "n_samples": len(p), + "kl_divergence": kl_divergence(t, p), + "js_divergence": js_divergence(t, p), + } + + return results + + +# ============ 预训练权重加载 ============ + +def load_pretrain_weights_to_model( + model: Union[LNPModel, LNPModelWithoutMPNN], + pretrain_state_dict: Dict, + d_model: int, + pretrain_config: Dict, + load_delivery_head: bool = True, +) -> bool: + """ + 加载预训练权重到模型。 + + Returns: + 是否成功加载 + """ + if pretrain_config.get("d_model") != d_model: + logger.warning( + f"d_model mismatch: pretrain={pretrain_config.get('d_model')}, " + f"current={d_model}. Skipping pretrain loading." + ) + return False + + model.load_pretrain_weights( + pretrain_state_dict=pretrain_state_dict, + load_delivery_head=load_delivery_head, + strict=False, + ) + return True + + +# ============ 内层 Optuna 调参 ============ +def run_inner_optuna( + full_dataset: LNPDataset, + inner_train_indices: np.ndarray, + strata: np.ndarray, + device: torch.device, + n_trials: int = 20, + epochs_per_trial: int = 30, + patience: int = 10, + batch_size: int = 32, + n_inner_folds: int = 3, + use_mpnn: bool = False, + seed: int = 42, + study_path: Optional[Path] = None, + pretrain_state_dict: Optional[Dict] = None, + pretrain_config: Optional[Dict] = None, + load_delivery_head: bool = True, + rdkit_cache: Optional[Dict] = None, + use_moe: bool = False, + moe_n_experts: int = 4, + moe_top_k: int = 2, + moe_expert_hidden_mult: int = 2, + moe_jitter_noise: float = 0.0, + set_transformer_block: str = "sab", + llm_kwargs: Optional[Dict] = None, +) -> Tuple[Dict, int, optuna.Study]: + """ + 在内层数据上运行 Optuna 超参搜索。 + + Args: + full_dataset: 完整数据集 + inner_train_indices: 内层训练数据的索引（相对于 full_dataset） + strata: 每个样本的分层标签 + device: 设备 + n_trials: Optuna 试验数 + epochs_per_trial: 每个试验的最大 epoch + patience: 早停耐心值 + batch_size: 批次大小 + n_inner_folds: 内层折数 + use_mpnn: 是否使用 MPNN + seed: 随机种子 + study_path: 可选的 study 持久化路径 + pretrain_state_dict: 预训练权重 + pretrain_config: 预训练配置 + load_delivery_head: 是否加载 delivery head 权重 + + Returns: + (best_params, epoch_mean, study) + """ + if optuna is None: + raise ImportError("Optuna not installed. Run: pip install optuna") + + inner_strata = strata[inner_train_indices] + + # 固定架构参数（与预训练一致，确保权重完整加载） + _cfg = pretrain_config or {} + fixed_d_model = _cfg.get("d_model", 256) + fixed_num_heads = _cfg.get("num_heads", 8) + fixed_n_attn_layers = _cfg.get("n_attn_layers", 4) + fixed_fusion_strategy = _cfg.get("fusion_strategy", "attention") + fixed_head_hidden_dim = _cfg.get("head_hidden_dim", 128) + logger.info( + f"Fixed architecture params: d_model={fixed_d_model}, num_heads={fixed_num_heads}, " + f"n_attn_layers={fixed_n_attn_layers}, fusion={fixed_fusion_strategy}, " + f"head_hidden_dim={fixed_head_hidden_dim}" + ) + + def objective(trial: optuna.Trial) -> float: + d_model = fixed_d_model + num_heads = fixed_num_heads + n_attn_layers = fixed_n_attn_layers + fusion_strategy = fixed_fusion_strategy + head_hidden_dim = fixed_head_hidden_dim + + # 搜索训练超参数 + dropout = trial.suggest_float("dropout", 0.1, 0.5) + lr = trial.suggest_float("lr", 1e-5, 1e-3, log=True) + weight_decay = trial.suggest_float("weight_decay", 1e-5, 1e-1, log=True) + backbone_lr_ratio = trial.suggest_float("backbone_lr_ratio", 0.01, 1.0, log=True) + + # MoE / LLM + use_llm = bool(llm_kwargs.get("use_llm", False)) + moe_ne_t = trial.suggest_categorical("moe_n_experts", [2, 4]) if use_moe else moe_n_experts + moe_tk_t = trial.suggest_int("moe_top_k", 1, 2) if use_moe else moe_top_k + moe_hm_t = trial.suggest_categorical("moe_expert_hidden_mult", [1, 2]) if use_moe else moe_expert_hidden_mult + llm_kwargs_t = dict(llm_kwargs) + if use_llm: + llm_kwargs_t["llm_use_lora"] = True + llm_kwargs_t["llm_lora_r"] = trial.suggest_categorical("llm_lora_r", [8, 16, 32]) + + # 内层 3-fold CV + inner_cv = StratifiedKFold( + n_splits=n_inner_folds, shuffle=True, random_state=seed + ) + + fold_val_losses = [] + fold_best_epochs = [] + + for inner_fold, (inner_train_idx, inner_val_idx) in enumerate( + inner_cv.split(inner_train_indices, inner_strata) + ): + # 获取实际的数据集索引 + actual_train_idx = inner_train_indices[inner_train_idx] + actual_val_idx = inner_train_indices[inner_val_idx] + + # 创建 DataLoader + train_subset = Subset(full_dataset, actual_train_idx.tolist()) + val_subset = Subset(full_dataset, actual_val_idx.tolist()) + + train_loader = DataLoader( + train_subset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn + ) + val_loader = DataLoader( + val_subset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn + ) + + # 计算类权重 + class_weights = compute_class_weights_from_loader(train_loader) + + # 创建模型 + model = create_model( + d_model=d_model, + num_heads=num_heads, + n_attn_layers=n_attn_layers, + fusion_strategy=fusion_strategy, + head_hidden_dim=head_hidden_dim, + dropout=dropout, + use_mpnn=use_mpnn, + mpnn_device=device.type, + use_moe=use_moe, + moe_n_experts=moe_ne_t, + moe_top_k=moe_tk_t, + moe_expert_hidden_mult=moe_hm_t, + moe_jitter_noise=moe_jitter_noise, + set_transformer_block=set_transformer_block, + llm_kwargs=llm_kwargs_t, + ) + if rdkit_cache is not None: + model.rdkit_encoder._cache = rdkit_cache + + # 加载预训练权重 + if pretrain_state_dict is not None and pretrain_config is not None: + load_pretrain_weights_to_model( + model, pretrain_state_dict, d_model, pretrain_config, load_delivery_head + ) + + # 训练（带早停） + result = train_with_early_stopping( + model=model, + train_loader=train_loader, + val_loader=val_loader, + device=device, + lr=lr, + weight_decay=weight_decay, + epochs=epochs_per_trial, + patience=patience, + class_weights=class_weights, + backbone_lr_ratio=backbone_lr_ratio, + ) + + fold_val_losses.append(result["best_val_loss"]) + fold_best_epochs.append(result["best_epoch"]) + + # 记录 epoch_mean 到 trial + epoch_mean = int(round(np.mean(fold_best_epochs))) + trial.set_user_attr("epoch_mean", epoch_mean) + trial.set_user_attr("fold_best_epochs", fold_best_epochs) + + return np.mean(fold_val_losses) + + # 创建 study + storage = None + if study_path is not None: + storage = f"sqlite:///{study_path}" + + study = optuna.create_study( + direction="minimize", + sampler=TPESampler(seed=seed), + storage=storage, + study_name="inner_optuna", + load_if_exists=True, + ) + + study.optimize(objective, n_trials=n_trials, show_progress_bar=True) + + best_params = dict(study.best_trial.params) + best_params.update({ + "d_model": fixed_d_model, + "num_heads": fixed_num_heads, + "n_attn_layers": fixed_n_attn_layers, + "fusion_strategy": fixed_fusion_strategy, + "head_hidden_dim": fixed_head_hidden_dim, + "set_transformer_block": set_transformer_block, + }) + epoch_mean = study.best_trial.user_attrs.get("epoch_mean", epochs_per_trial) + + logger.info(f"Best trial: {study.best_trial.number}") + logger.info(f"Best val_loss: {study.best_trial.value:.4f}") + logger.info(f"Best params: {best_params}") + logger.info(f"Epoch mean: {epoch_mean}") + + return best_params, epoch_mean, study + + +# ============ 单 fold 执行（可跨进程调用） ============ +def _run_single_outer_fold( + outer_fold: int, + outer_train_idx: np.ndarray, + outer_test_idx: np.ndarray, + df: pd.DataFrame, + strata: np.ndarray, + fold_dir: Path, + n_trials: int, + epochs_per_trial: int, + inner_patience: int, + batch_size: int, + n_inner_folds: int, + use_mpnn: bool, + seed: int, + pretrain_state_dict: Optional[Dict], + pretrain_config: Optional[Dict], + load_delivery_head: bool, + device_str: str, + use_moe: bool = False, + moe_n_experts: int = 4, + moe_top_k: int = 2, + moe_expert_hidden_mult: int = 2, + moe_jitter_noise: float = 0.0, + set_transformer_block: str = "sab", + llm_kwargs: Optional[Dict] = None, + precomputed_best_params: Optional[Dict] = None, + precomputed_epoch_mean: Optional[int] = None, +) -> Dict: + """ + 执行单个外层 fold 的完整流程（内层调参 + 外层训练 + 评估）。 + + 所有参数均为可序列化类型，以支持 spawn 多进程。 + """ + device = torch.device(device_str) + set_global_seed(seed + outer_fold) + fold_dir = Path(fold_dir) + fold_dir.mkdir(parents=True, exist_ok=True) + + full_dataset = LNPDataset(df) + + logger.info(f"\n{'='*60}") + logger.info(f"OUTER FOLD {outer_fold}") + logger.info(f"{'='*60}") + logger.info(f"Train: {len(outer_train_idx)}, Test: {len(outer_test_idx)}") + + # 预热 RDKit 缓存（在整个 fold 内共享） + rdkit_cache = warmup_rdkit_cache(full_dataset.smiles) + + # 保存 split indices + with open(fold_dir / "splits.json", "w") as f: + json.dump({ + "outer_train_idx": outer_train_idx.tolist(), + "outer_test_idx": outer_test_idx.tolist(), + }, f) + + # 内层 Optuna 调参（方式 B：已有超参则跳过，直接复用） + if precomputed_best_params is not None and precomputed_epoch_mean is not None: + logger.info("Reusing precomputed best_params (skip inner Optuna).") + best_params = dict(precomputed_best_params) + epoch_mean = int(precomputed_epoch_mean) + else: + logger.info(f"\nRunning inner Optuna with {n_trials} trials...") + study_path = fold_dir / "optuna_study.sqlite3" + best_params, epoch_mean, study = run_inner_optuna( + full_dataset=full_dataset, + inner_train_indices=outer_train_idx, + strata=strata, + device=device, + n_trials=n_trials, + epochs_per_trial=epochs_per_trial, + patience=inner_patience, + batch_size=batch_size, + n_inner_folds=n_inner_folds, + use_mpnn=use_mpnn, + seed=seed + outer_fold, + study_path=study_path, + pretrain_state_dict=pretrain_state_dict, + pretrain_config=pretrain_config, + load_delivery_head=load_delivery_head, + rdkit_cache=rdkit_cache, + use_moe=use_moe, + moe_n_experts=moe_n_experts, + moe_top_k=moe_top_k, + moe_expert_hidden_mult=moe_expert_hidden_mult, + moe_jitter_noise=moe_jitter_noise, + set_transformer_block=set_transformer_block, + llm_kwargs=llm_kwargs, + ) + + # 保存最佳参数 + with open(fold_dir / "best_params.json", "w") as f: + json.dump(best_params, f, indent=2) + with open(fold_dir / "epoch_mean.json", "w") as f: + json.dump({"epoch_mean": epoch_mean}, f) + + # 外层训练（使用最优超参，固定 epoch 数，不 early-stop） + logger.info(f"\nTraining outer fold with best params, epochs={epoch_mean}...") + + # 从 outer_train 再切 10% 作为"监控用 val"（只画曲线、看 gap，不参与选模型/早停） + _rng = np.random.RandomState(seed + outer_fold) + _perm = _rng.permutation(len(outer_train_idx)) + _n_val = max(1, int(0.1 * len(outer_train_idx))) + fit_idx = outer_train_idx[_perm[_n_val:]] + monitor_idx = outer_train_idx[_perm[:_n_val]] + + train_subset = Subset(full_dataset, fit_idx.tolist()) + monitor_subset = Subset(full_dataset, monitor_idx.tolist()) + test_subset = Subset(full_dataset, outer_test_idx.tolist()) + + train_loader = DataLoader( + train_subset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn + ) + monitor_loader = DataLoader( + monitor_subset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn + ) + test_loader = DataLoader( + test_subset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn + ) + + class_weights = compute_class_weights_from_loader(train_loader) + + model = create_model( + d_model=best_params["d_model"], + num_heads=best_params["num_heads"], + n_attn_layers=best_params["n_attn_layers"], + fusion_strategy=best_params["fusion_strategy"], + head_hidden_dim=best_params["head_hidden_dim"], + dropout=best_params["dropout"], + use_mpnn=use_mpnn, + mpnn_device=device.type, + use_moe=use_moe, + moe_n_experts=best_params.get("moe_n_experts", moe_n_experts), + moe_top_k=best_params.get("moe_top_k", moe_top_k), + moe_expert_hidden_mult=best_params.get("moe_expert_hidden_mult", moe_expert_hidden_mult), + moe_jitter_noise=moe_jitter_noise, + set_transformer_block=best_params.get("set_transformer_block", "sab"), + llm_kwargs={**llm_kwargs, **({"llm_use_lora": True, "llm_lora_r": best_params["llm_lora_r"]} + if (llm_kwargs.get("use_llm", False) and "llm_lora_r" in best_params) else {})}, + ) + model.rdkit_encoder._cache = rdkit_cache + + if pretrain_state_dict is not None and pretrain_config is not None: + loaded = load_pretrain_weights_to_model( + model, pretrain_state_dict, best_params["d_model"], + pretrain_config, load_delivery_head + ) + if loaded: + logger.info(f"Loaded pretrain weights for outer fold {outer_fold}") + + train_result = train_fixed_epochs( + model=model, + train_loader=train_loader, + val_loader=monitor_loader, + device=device, + lr=best_params["lr"], + weight_decay=best_params["weight_decay"], + epochs=epoch_mean, + class_weights=class_weights, + use_cosine_annealing=True, + backbone_lr_ratio=best_params.get("backbone_lr_ratio", 1.0), + freeze_backbone_epochs=3, + ) + + model.load_state_dict(train_result["final_state"]) + model = model.to(device) + + config = { + "d_model": best_params["d_model"], + "num_heads": best_params["num_heads"], + "n_attn_layers": best_params["n_attn_layers"], + "fusion_strategy": best_params["fusion_strategy"], + "head_hidden_dim": best_params["head_hidden_dim"], + "set_transformer_block": best_params.get("set_transformer_block", "sab"), + "dropout": best_params["dropout"], + "use_mpnn": use_mpnn, + "use_moe": use_moe, + "moe_n_experts": moe_n_experts, + "moe_top_k": moe_top_k, + "moe_expert_hidden_mult": moe_expert_hidden_mult, + "moe_jitter_noise": moe_jitter_noise, + **(llm_kwargs or {}), + } + + torch.save({ + "model_state_dict": train_result["final_state"], + "config": config, + "epoch_mean": epoch_mean, + "best_params": best_params, + }, fold_dir / "model.pt") + + with open(fold_dir / "history.json", "w") as f: + json.dump(train_result["history"], f, indent=2) + + # 在测试集上评估 + logger.info("Evaluating on outer test set...") + test_metrics = evaluate_on_test(model, test_loader, device) + + with open(fold_dir / "test_metrics.json", "w") as f: + json.dump(test_metrics, f, indent=2) + + logger.info(f"\nOuter Fold {outer_fold} Test Results:") + for task, metrics in test_metrics.items(): + if "rmse" in metrics: + logger.info(f" {task}: RMSE={metrics['rmse']:.4f}, R²={metrics['r2']:.4f}") + elif "accuracy" in metrics: + logger.info(f" {task}: Acc={metrics['accuracy']:.4f}, F1={metrics['f1']:.4f}") + elif "kl_divergence" in metrics: + logger.info(f" {task}: KL={metrics['kl_divergence']:.4f}, JS={metrics['js_divergence']:.4f}") + + return { + "fold": outer_fold, + "best_params": best_params, + "epoch_mean": epoch_mean, + "test_metrics": test_metrics, + } + + +# ============ 主流程 ============ +@app.command() +def main( + input_path: Path = INTERIM_DATA_DIR / "internal.csv", + output_dir: Path = MODELS_DIR / "nested_cv", + # CV 参数 + n_outer_folds: int = 5, + n_inner_folds: int = 3, + min_stratum_count: int = 5, + seed: int = 42, + # Optuna 参数 + n_trials: int = 20, + epochs_per_trial: int = 30, + inner_patience: int = 10, + # 训练参数 + batch_size: int = 32, + # 预训练权重 + init_from_pretrain: Optional[Path] = None, + load_delivery_head: bool = False, + # MPNN + use_mpnn: bool = False, + n_repeats: int = 1, + repeat_seed_step: int = 1000, + # MoE（消融开关） + use_moe: bool = False, + moe_n_experts: int = 4, + moe_top_k: int = 2, + moe_expert_hidden_mult: int = 2, + moe_jitter_noise: float = 0.0, + # Set Transformer + set_transformer_block: str = "sab", + # LLM（消融开关） + use_llm: bool = False, + llm_model_path: str = DEFAULT_MOLT5_PATH, + llm_freeze: bool = True, + llm_use_lora: bool = False, + llm_lora_r: int = 8, + llm_lora_alpha: int = 16, + llm_lora_dropout: float = 0.05, + # 并行 + parallel: bool = False, + # 设备 + device: str = "cuda" if torch.cuda.is_available() else "cpu", +): + """ + 嵌套交叉验证 + Optuna 超参调优。 + + 外层 5-fold（20% test / 80% train），内层 3-fold Optuna 调参。 + 外层训练不使用 early-stopping，epoch 数使用内层 best trial 的 epoch_mean。 + + 使用 --init-from-pretrain 从预训练 checkpoint 初始化模型权重。 + 使用 --parallel 同时运行所有外层 fold（需要足够 GPU 显存）。 + """ + if optuna is None: + logger.error("Optuna not installed. Run: pip install optuna") + raise typer.Exit(1) + + logger.info(f"Using device: {device}") + device = torch.device(device) + set_global_seed(seed) + + llm_kwargs = dict( + use_llm=use_llm, + llm_model_path=llm_model_path, + llm_freeze=llm_freeze, + llm_use_lora=llm_use_lora, + llm_lora_r=llm_lora_r, + llm_lora_alpha=llm_lora_alpha, + llm_lora_dropout=llm_lora_dropout, + ) + + # 加载预训练权重（如果指定） + pretrain_state_dict = None + pretrain_config = None + 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", 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')})") + else: + logger.warning(f"Pretrain checkpoint not found: {init_from_pretrain}, skipping") + + # 创建输出目录（带时间戳） + run_name = datetime.now().strftime("%Y%m%d_%H%M%S") + run_dir = output_dir / run_name + run_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"Output directory: {run_dir}") + + # 加载数据 + logger.info(f"Loading data from {input_path}") + df = pd.read_csv(input_path) + logger.info(f"Loaded {len(df)} samples") + + # 处理数据 + logger.info("Processing dataframe...") + df = process_dataframe(df) + + # 构建复合分层标签 + logger.info("Building composite strata...") + strata, strata_info = build_composite_strata(df, min_stratum_count) + + # 保存 strata 信息 + with open(run_dir / "strata_info.json", "w") as f: + json.dump(strata_info, f, indent=2, default=str) + + # 创建完整数据集（仅用于获取样本数做 split） + n_samples = len(LNPDataset(df)) + + # 外层 CV split + outer_cv = StratifiedKFold( + n_splits=n_outer_folds, shuffle=True, random_state=seed + ) + + device_str = str(device) + fold_args = [] + for outer_fold, (outer_train_idx, outer_test_idx) in enumerate( + outer_cv.split(np.arange(n_samples), strata) + ): + fold_args.append(dict( + outer_fold=outer_fold, + outer_train_idx=outer_train_idx, + outer_test_idx=outer_test_idx, + df=df, + strata=strata, + fold_dir=run_dir / f"outer_fold_{outer_fold}", + n_trials=n_trials, + epochs_per_trial=epochs_per_trial, + inner_patience=inner_patience, + batch_size=batch_size, + n_inner_folds=n_inner_folds, + use_mpnn=use_mpnn, + seed=seed, + pretrain_state_dict=pretrain_state_dict, + pretrain_config=pretrain_config, + load_delivery_head=load_delivery_head, + device_str=device_str, + use_moe=use_moe, + moe_n_experts=moe_n_experts, + moe_top_k=moe_top_k, + moe_expert_hidden_mult=moe_expert_hidden_mult, + moe_jitter_noise=moe_jitter_noise, + set_transformer_block=set_transformer_block, + llm_kwargs=llm_kwargs, + )) + + if parallel: + import multiprocessing as mp + from concurrent.futures import ProcessPoolExecutor + + ctx = mp.get_context("spawn") + logger.info(f"Running {n_outer_folds} outer folds in PARALLEL (spawn)") + with ProcessPoolExecutor(max_workers=n_outer_folds, mp_context=ctx) as executor: + futures = [executor.submit(_run_single_outer_fold, **args) for args in fold_args] + outer_results = [f.result() for f in futures] + outer_results.sort(key=lambda r: r["fold"]) + else: + logger.info(f"Running {n_outer_folds} outer folds SEQUENTIALLY") + outer_results = [] + per_fold_params = {} + for args in fold_args: + result = _run_single_outer_fold(**args) + outer_results.append(result) + per_fold_params[result["fold"]] = (result["best_params"], result["epoch_mean"]) + + for rep in range(1, n_repeats): + logger.info(f"\n===== Repeat {rep}/{n_repeats - 1} (reuse hyperparams) =====") + for args in fold_args: + bp, em = per_fold_params[args["outer_fold"]] + rep_args = dict(args) + rep_args["seed"] = seed + rep * repeat_seed_step + rep_args["fold_dir"] = run_dir / f"repeat_{rep}" / f"outer_fold_{args['outer_fold']}" + rep_args["precomputed_best_params"] = bp + rep_args["precomputed_epoch_mean"] = em + outer_results.append(_run_single_outer_fold(**rep_args)) + + # 汇总结果 + logger.info("\n" + "=" * 60) + logger.info("NESTED CV COMPLETE") + logger.info("=" * 60) + + # 计算汇总统计 + summary = {"fold_results": outer_results} + + # 对每个任务计算均值和标准差 + tasks_with_metrics = {} + for result in outer_results: + for task, metrics in result["test_metrics"].items(): + if task not in tasks_with_metrics: + tasks_with_metrics[task] = {k: [] for k in metrics.keys() if k != "n_samples"} + for k, v in metrics.items(): + if k != "n_samples": + tasks_with_metrics[task][k].append(v) + + summary["summary_stats"] = {} + for task, metrics_dict in tasks_with_metrics.items(): + summary["summary_stats"][task] = {} + for metric_name, values in metrics_dict.items(): + summary["summary_stats"][task][f"{metric_name}_mean"] = float(np.mean(values)) + summary["summary_stats"][task][f"{metric_name}_std"] = float(np.std(values)) + + # 打印汇总 + logger.info("\n[Summary Statistics]") + for task, stats in summary["summary_stats"].items(): + if "rmse_mean" in stats: + logger.info( + f" {task}: RMSE={stats['rmse_mean']:.4f}±{stats['rmse_std']:.4f}, " + f"R²={stats['r2_mean']:.4f}±{stats['r2_std']:.4f}" + ) + elif "accuracy_mean" in stats: + logger.info( + f" {task}: Acc={stats['accuracy_mean']:.4f}±{stats['accuracy_std']:.4f}, " + f"F1={stats['f1_mean']:.4f}±{stats['f1_std']:.4f}" + ) + elif "kl_divergence_mean" in stats: + logger.info( + f" {task}: KL={stats['kl_divergence_mean']:.4f}±{stats['kl_divergence_std']:.4f}, " + f"JS={stats['js_divergence_mean']:.4f}±{stats['js_divergence_std']:.4f}" + ) + + # 保存汇总 + with open(run_dir / "summary.json", "w") as f: + json.dump(summary, f, indent=2) + + logger.success(f"\nAll results saved to {run_dir}") + + +if __name__ == "__main__": + app() + diff --git a/lnp_ml/modeling/pretrain.py b/lnp_ml/modeling/pretrain.py index b7d9d66..54a39ea 100644 --- a/lnp_ml/modeling/pretrain.py +++ b/lnp_ml/modeling/pretrain.py @@ -1,521 +1,574 @@ -"""预训练脚本：在外部 LiON 数据上预训练 backbone + delivery head""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -import pandas as pd -import torch -import torch.nn as nn -from torch.utils.data import DataLoader -from loguru import logger -from tqdm import tqdm -import typer - -from lnp_ml.config import MODELS_DIR, PROCESSED_DATA_DIR -from lnp_ml.dataset import ExternalDeliveryDataset, collate_fn -from lnp_ml.modeling.visualization import plot_loss_curves - -# MPNN ensemble 默认路径 -DEFAULT_MPNN_ENSEMBLE_DIR = MODELS_DIR / "mpnn" / "all_amine_split_for_LiON" - - -def find_mpnn_ensemble_paths(base_dir: Path = DEFAULT_MPNN_ENSEMBLE_DIR) -> List[str]: - """ - 自动查找 MPNN ensemble 的 model.pt 文件。 - - 在 base_dir 下查找所有 cv_*/fold_*/model_*/model.pt 文件。 - """ - model_paths = sorted(base_dir.glob("cv_*/fold_*/model_*/model.pt")) - if not model_paths: - raise FileNotFoundError(f"No model.pt files found in {base_dir}") - return [str(p) for p in model_paths] -from lnp_ml.modeling.models import LNPModel, LNPModelWithoutMPNN - - -app = typer.Typer() - - -class EarlyStopping: - """早停机制""" - - def __init__(self, patience: int = 10, min_delta: float = 0.0): - self.patience = patience - self.min_delta = min_delta - self.counter = 0 - self.best_loss = float("inf") - - def __call__(self, val_loss: float) -> bool: - if val_loss < self.best_loss - self.min_delta: - self.best_loss = val_loss - self.counter = 0 - return False - self.counter += 1 - return self.counter >= self.patience - - -def warmup_cache(model: nn.Module, smiles_list: List[str], batch_size: int = 256) -> None: - """ - 预热 RDKit 特征缓存，避免训练时计算阻塞。 - """ - unique_smiles = list(set(smiles_list)) - logger.info(f"Warming up RDKit cache for {len(unique_smiles)} unique SMILES...") - - for i in tqdm(range(0, len(unique_smiles), batch_size), desc="Cache warmup"): - batch = unique_smiles[i:i + batch_size] - model.rdkit_encoder(batch) - - logger.success(f"Cache warmup complete. Cached {len(model.rdkit_encoder._cache)} SMILES.") - - -def train_epoch_delivery( - model: nn.Module, - loader: DataLoader, - optimizer: torch.optim.Optimizer, - device: torch.device, - epoch: int = 0, -) -> Dict[str, float]: - """ - 单个 epoch 的预训练（仅 delivery 任务）。 - """ - model.train() - total_loss = 0.0 - n_samples = 0 - - pbar = tqdm(loader, desc=f"Epoch {epoch+1} [Train]", leave=False) - for batch in pbar: - smiles = batch["smiles"] - tabular = {k: v.to(device) for k, v in batch["tabular"].items()} - targets = batch["targets"]["delivery"].to(device) - mask = batch["mask"]["delivery"].to(device) - - optimizer.zero_grad() - - # Forward: 只预测 delivery - pred = model.forward_delivery(smiles, tabular) # [B, 1] - pred = pred.squeeze(-1) # [B] - - # 计算损失（仅对有效样本） - if mask.any(): - loss = nn.functional.mse_loss(pred[mask], targets[mask]) - loss.backward() - optimizer.step() - total_loss += loss.item() * mask.sum().item() - n_samples += mask.sum().item() - - pbar.set_postfix({"loss": total_loss / max(n_samples, 1)}) - - avg_loss = total_loss / max(n_samples, 1) - return {"loss": avg_loss, "n_samples": n_samples} - - -@torch.no_grad() -def validate_delivery( - model: nn.Module, - loader: DataLoader, - device: torch.device, -) -> Dict[str, float]: - """ - 验证（仅 delivery 任务）。 - """ - model.eval() - total_loss = 0.0 - n_samples = 0 - - for batch in loader: - smiles = batch["smiles"] - tabular = {k: v.to(device) for k, v in batch["tabular"].items()} - targets = batch["targets"]["delivery"].to(device) - mask = batch["mask"]["delivery"].to(device) - - pred = model.forward_delivery(smiles, tabular).squeeze(-1) - - if mask.any(): - loss = nn.functional.mse_loss(pred[mask], targets[mask]) - total_loss += loss.item() * mask.sum().item() - n_samples += mask.sum().item() - - avg_loss = total_loss / max(n_samples, 1) - return {"loss": avg_loss, "n_samples": n_samples} - - -def pretrain( - train_loader: DataLoader, - val_loader: DataLoader, - model: nn.Module, - device: torch.device, - lr: float = 1e-4, - weight_decay: float = 1e-5, - epochs: int = 50, - patience: int = 10, -) -> dict: - """ - 预训练循环。 - - Returns: - 训练历史和最佳验证损失 - """ - model = model.to(device) - optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay) - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, mode="min", factor=0.5, patience=5 - ) - early_stopping = EarlyStopping(patience=patience) - - history = {"train": [], "val": []} - best_val_loss = float("inf") - best_state = None - - for epoch in range(epochs): - # Train - train_metrics = train_epoch_delivery(model, train_loader, optimizer, device, epoch) - - # Validate - val_metrics = validate_delivery(model, val_loader, device) - - # Log - logger.info( - f"Epoch {epoch + 1}/{epochs} | " - f"Train Loss: {train_metrics['loss']:.4f} | " - f"Val Loss: {val_metrics['loss']:.4f}" - ) - - history["train"].append(train_metrics) - history["val"].append(val_metrics) - - # Learning rate scheduling - scheduler.step(val_metrics["loss"]) - - # Save best model - if val_metrics["loss"] < best_val_loss: - best_val_loss = val_metrics["loss"] - best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()} - logger.info(f" -> New best model (val_loss={best_val_loss:.4f})") - - # Early stopping - if early_stopping(val_metrics["loss"]): - logger.info(f"Early stopping at epoch {epoch + 1}") - break - - # Restore best model - if best_state is not None: - model.load_state_dict(best_state) - - return { - "history": history, - "best_val_loss": best_val_loss, - } - - -@app.command() -def main( - # 数据路径（已处理的 parquet 文件） - train_path: Path = PROCESSED_DATA_DIR / "train_pretrain.parquet", - val_path: Path = PROCESSED_DATA_DIR / "val_pretrain.parquet", - output_dir: Path = MODELS_DIR, - # 模型参数 - d_model: int = 256, - num_heads: int = 8, - n_attn_layers: int = 4, - fusion_strategy: str = "attention", - head_hidden_dim: int = 128, - dropout: float = 0.1, - # MPNN 参数（可选） - use_mpnn: bool = False, # 启用 MPNN，自动从默认路径加载 ensemble - mpnn_checkpoint: Optional[str] = None, - mpnn_ensemble_paths: Optional[str] = None, # 逗号分隔的路径列表 - mpnn_device: str = "cpu", - # 训练参数 - batch_size: int = 64, - lr: float = 1e-4, - weight_decay: float = 1e-5, - epochs: int = 50, - patience: int = 10, - # 设备 - device: str = "cuda" if torch.cuda.is_available() else "cpu", -): - """ - 在外部 LiON 数据上预训练 backbone + delivery head。 - - 需要先运行 `make data_pretrain` 生成 parquet 文件。 - - 使用 --use-mpnn 启用 MPNN encoder（自动从 models/mpnn/all_amine_split_for_LiON 加载）。 - - 产出: - - models/pretrain_delivery.pt: 包含 backbone + delivery head 权重 - - models/pretrain_history.json: 训练历史 - """ - logger.info(f"Using device: {device}") - device_obj = torch.device(device) - - # 加载已处理的 parquet 文件 - logger.info(f"Loading train data from {train_path}") - train_df = pd.read_parquet(train_path) - train_dataset = ExternalDeliveryDataset(train_df) - - logger.info(f"Loading val data from {val_path}") - val_df = pd.read_parquet(val_path) - val_dataset = ExternalDeliveryDataset(val_df) - - logger.info(f"Train samples: {len(train_dataset)}, Val samples: {len(val_dataset)}") - - train_loader = DataLoader( - train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn - ) - val_loader = DataLoader( - val_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn - ) - - # 解析 MPNN 配置 - # 优先级：mpnn_checkpoint > mpnn_ensemble_paths > use_mpnn（自动查找） - ensemble_paths_list = None - if mpnn_ensemble_paths: - ensemble_paths_list = mpnn_ensemble_paths.split(",") - elif use_mpnn and mpnn_checkpoint is None: - # --use-mpnn 但没有指定具体路径，自动查找 - logger.info(f"Auto-detecting MPNN ensemble from {DEFAULT_MPNN_ENSEMBLE_DIR}") - ensemble_paths_list = find_mpnn_ensemble_paths() - logger.info(f"Found {len(ensemble_paths_list)} MPNN models") - - enable_mpnn = mpnn_checkpoint is not None or ensemble_paths_list is not None - - # 创建模型 - logger.info(f"Creating model (use_mpnn={enable_mpnn})...") - if enable_mpnn: - model = LNPModel( - d_model=d_model, - num_heads=num_heads, - n_attn_layers=n_attn_layers, - fusion_strategy=fusion_strategy, - head_hidden_dim=head_hidden_dim, - dropout=dropout, - mpnn_checkpoint=mpnn_checkpoint, - mpnn_ensemble_paths=ensemble_paths_list, - mpnn_device=mpnn_device, - ) - else: - model = LNPModelWithoutMPNN( - d_model=d_model, - num_heads=num_heads, - n_attn_layers=n_attn_layers, - fusion_strategy=fusion_strategy, - head_hidden_dim=head_hidden_dim, - dropout=dropout, - ) - - n_params_total = sum(p.numel() for p in model.parameters()) - n_params_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) - logger.info(f"Model parameters: {n_params_total:,} total, {n_params_trainable:,} trainable") - - # 预热 RDKit 缓存（避免训练时阻塞） - all_smiles = train_df["smiles"].tolist() + val_df["smiles"].tolist() - warmup_cache(model, all_smiles, batch_size=256) - - # 预训练 - logger.info("Starting pretraining on external data (delivery only)...") - result = pretrain( - train_loader=train_loader, - val_loader=val_loader, - model=model, - device=device_obj, - lr=lr, - weight_decay=weight_decay, - epochs=epochs, - patience=patience, - ) - - # 保存预训练 checkpoint - output_dir.mkdir(parents=True, exist_ok=True) - checkpoint_path = output_dir / "pretrain_delivery.pt" - torch.save( - { - "model_state_dict": model.state_dict(), - "backbone_state_dict": model.get_backbone_state_dict(), - "delivery_head_state_dict": model.get_delivery_head_state_dict(), - "config": { - "d_model": d_model, - "num_heads": num_heads, - "n_attn_layers": n_attn_layers, - "fusion_strategy": fusion_strategy, - "head_hidden_dim": head_hidden_dim, - "dropout": dropout, - "use_mpnn": enable_mpnn, - }, - "best_val_loss": result["best_val_loss"], - }, - checkpoint_path, - ) - logger.success(f"Saved pretrain checkpoint to {checkpoint_path}") - - # 保存训练历史 - history_path = output_dir / "pretrain_history.json" - with open(history_path, "w") as f: - json.dump(result["history"], f, indent=2) - logger.success(f"Saved pretrain history to {history_path}") - - # 绘制 loss 曲线图 - loss_plot_path = output_dir / "pretrain_loss_curves.png" - plot_loss_curves( - history=result["history"], - output_path=loss_plot_path, - title="Pretrain Loss Curves (Delivery)", - ) - logger.success(f"Saved loss curves plot to {loss_plot_path}") - - logger.success( - f"Pretraining complete! Best val_loss: {result['best_val_loss']:.4f}" - ) - - -@app.command() -def test( - # 数据路径 - val_path: Path = PROCESSED_DATA_DIR / "val_pretrain.parquet", - model_path: Path = MODELS_DIR / "pretrain_delivery.pt", - output_path: Path = MODELS_DIR / "pretrain_test_results.json", - # MPNN 参数 - use_mpnn: bool = False, - mpnn_device: str = "cpu", - # 其他参数 - batch_size: int = 64, - device: str = "cuda" if torch.cuda.is_available() else "cpu", -): - """ - 评估 pretrain 模型在外部数据上的 delivery 预测性能。 - - 输出详细指标：MSE, RMSE, MAE, R² - """ - import numpy as np - from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score - - logger.info(f"Using device: {device}") - device_obj = torch.device(device) - - # 加载模型 - logger.info(f"Loading pretrain model from {model_path}") - checkpoint = torch.load(model_path, map_location=device_obj, weights_only=False) - config = checkpoint["config"] - - # 解析 MPNN 配置 - enable_mpnn = config.get("use_mpnn", False) - if enable_mpnn or use_mpnn: - logger.info(f"Auto-detecting MPNN ensemble from {DEFAULT_MPNN_ENSEMBLE_DIR}") - ensemble_paths = find_mpnn_ensemble_paths() - logger.info(f"Found {len(ensemble_paths)} MPNN models") - - model = LNPModel( - d_model=config["d_model"], - num_heads=config["num_heads"], - n_attn_layers=config["n_attn_layers"], - fusion_strategy=config["fusion_strategy"], - head_hidden_dim=config["head_hidden_dim"], - dropout=config["dropout"], - mpnn_ensemble_paths=ensemble_paths, - mpnn_device=mpnn_device, - ) - else: - model = LNPModelWithoutMPNN( - d_model=config["d_model"], - num_heads=config["num_heads"], - n_attn_layers=config["n_attn_layers"], - fusion_strategy=config["fusion_strategy"], - head_hidden_dim=config["head_hidden_dim"], - dropout=config["dropout"], - ) - - model.load_state_dict(checkpoint["model_state_dict"]) - model.to(device_obj) - model.eval() - - logger.info(f"Model config: {config}") - logger.info(f"Best val_loss from training: {checkpoint.get('best_val_loss', 'N/A')}") - - # 加载数据 - logger.info(f"Loading validation data from {val_path}") - val_df = pd.read_parquet(val_path) - val_dataset = ExternalDeliveryDataset(val_df) - val_loader = DataLoader( - val_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn - ) - logger.info(f"Validation samples: {len(val_dataset)}") - - # 预测 - logger.info("Running predictions...") - all_preds = [] - all_targets = [] - all_masks = [] - - with torch.no_grad(): - for batch in tqdm(val_loader, desc="Predicting"): - smiles = batch["smiles"] - tabular = {k: v.to(device_obj) for k, v in batch["tabular"].items()} - targets = batch["targets"]["delivery"].numpy() - mask = batch["mask"]["delivery"].numpy() - - pred = model.forward_delivery(smiles, tabular).squeeze(-1).cpu().numpy() - - all_preds.extend(pred) - all_targets.extend(targets) - all_masks.extend(mask) - - # 转为数组 - all_preds = np.array(all_preds) - all_targets = np.array(all_targets) - all_masks = np.array(all_masks, dtype=bool) - - # 只计算有效样本 - y_pred = all_preds[all_masks] - y_true = all_targets[all_masks] - - # 计算指标 - mse = float(mean_squared_error(y_true, y_pred)) - rmse = float(np.sqrt(mse)) - mae = float(mean_absolute_error(y_true, y_pred)) - r2 = float(r2_score(y_true, y_pred)) - - # 额外统计 - correlation = float(np.corrcoef(y_true, y_pred)[0, 1]) - - results = { - "model_path": str(model_path), - "val_path": str(val_path), - "n_samples": int(all_masks.sum()), - "metrics": { - "mse": mse, - "rmse": rmse, - "mae": mae, - "r2": r2, - "correlation": correlation, - }, - "statistics": { - "y_true_mean": float(y_true.mean()), - "y_true_std": float(y_true.std()), - "y_pred_mean": float(y_pred.mean()), - "y_pred_std": float(y_pred.std()), - } - } - - # 打印结果 - logger.info("\n" + "=" * 50) - logger.info("PRETRAIN MODEL EVALUATION (Delivery)") - logger.info("=" * 50) - logger.info(f"Samples: {results['n_samples']}") - logger.info("\n[Metrics]") - logger.info(f" MSE: {mse:.4f}") - logger.info(f" RMSE: {rmse:.4f}") - logger.info(f" MAE: {mae:.4f}") - logger.info(f" R²: {r2:.4f}") - logger.info(f" Correlation: {correlation:.4f}") - logger.info("\n[Statistics]") - logger.info(f" True: mean={y_true.mean():.4f}, std={y_true.std():.4f}") - logger.info(f" Pred: mean={y_pred.mean():.4f}, std={y_pred.std():.4f}") - - # 保存结果 - with open(output_path, "w") as f: - json.dump(results, f, indent=2) - logger.success(f"\nSaved results to {output_path}") - - -if __name__ == "__main__": - app() - +"""预训练脚本：在外部 LiON 数据上预训练 backbone + delivery head""" + +import json +from pathlib import Path +from typing import Dict, List, Optional + +import pandas as pd +import torch +import torch.nn as nn +from torch.utils.data import DataLoader +from loguru import logger +from tqdm import tqdm +import typer + +from lnp_ml.config import MODELS_DIR, PROCESSED_DATA_DIR +from lnp_ml.dataset import ExternalDeliveryDataset, collate_fn +from lnp_ml.modeling.visualization import plot_loss_curves + +# MPNN ensemble 默认路径 +DEFAULT_MPNN_ENSEMBLE_DIR = MODELS_DIR / "mpnn" / "all_amine_split_for_LiON" + + +def find_mpnn_ensemble_paths(base_dir: Path = DEFAULT_MPNN_ENSEMBLE_DIR) -> List[str]: + """ + 自动查找 MPNN ensemble 的 model.pt 文件。 + + 在 base_dir 下查找所有 cv_*/fold_*/model_*/model.pt 文件。 + """ + model_paths = sorted(base_dir.glob("cv_*/fold_*/model_*/model.pt")) + if not model_paths: + raise FileNotFoundError(f"No model.pt files found in {base_dir}") + return [str(p) for p in model_paths] +from lnp_ml.modeling.models import LNPModel, LNPModelWithoutMPNN +from lnp_ml.modeling.layers.llm_prompt import DEFAULT_MOLT5_PATH +from lnp_ml.utils.seed import set_global_seed + + +app = typer.Typer() + + +class EarlyStopping: + """早停机制""" + + def __init__(self, patience: int = 10, min_delta: float = 0.0): + self.patience = patience + self.min_delta = min_delta + self.counter = 0 + self.best_loss = float("inf") + + def __call__(self, val_loss: float) -> bool: + if val_loss < self.best_loss - self.min_delta: + self.best_loss = val_loss + self.counter = 0 + return False + self.counter += 1 + return self.counter >= self.patience + + +def warmup_cache(model: nn.Module, smiles_list: List[str], batch_size: int = 256) -> None: + """ + 预热 RDKit 特征缓存，避免训练时计算阻塞。 + """ + unique_smiles = list(set(smiles_list)) + logger.info(f"Warming up RDKit cache for {len(unique_smiles)} unique SMILES...") + + for i in tqdm(range(0, len(unique_smiles), batch_size), desc="Cache warmup"): + batch = unique_smiles[i:i + batch_size] + model.rdkit_encoder(batch) + + logger.success(f"Cache warmup complete. Cached {len(model.rdkit_encoder._cache)} SMILES.") + + +def train_epoch_delivery( + model: nn.Module, + loader: DataLoader, + optimizer: torch.optim.Optimizer, + device: torch.device, + epoch: int = 0, +) -> Dict[str, float]: + """ + 单个 epoch 的预训练（仅 delivery 任务）。 + """ + model.train() + total_loss = 0.0 + n_samples = 0 + + pbar = tqdm(loader, desc=f"Epoch {epoch+1} [Train]", leave=False) + for batch in pbar: + smiles = batch["smiles"] + tabular = {k: v.to(device) for k, v in batch["tabular"].items()} + targets = batch["targets"]["delivery"].to(device) + mask = batch["mask"]["delivery"].to(device) + + optimizer.zero_grad() + + # Forward: 只预测 delivery + pred = model.forward_delivery(smiles, tabular) # [B, 1] + pred = pred.squeeze(-1) # [B] + + # 计算损失（仅对有效样本） + if mask.any(): + loss = nn.functional.mse_loss(pred[mask], targets[mask]) + loss.backward() + optimizer.step() + total_loss += loss.item() * mask.sum().item() + n_samples += mask.sum().item() + + pbar.set_postfix({"loss": total_loss / max(n_samples, 1)}) + + avg_loss = total_loss / max(n_samples, 1) + return {"loss": avg_loss, "n_samples": n_samples} + + +@torch.no_grad() +def validate_delivery( + model: nn.Module, + loader: DataLoader, + device: torch.device, +) -> Dict[str, float]: + """ + 验证（仅 delivery 任务）。 + """ + model.eval() + total_loss = 0.0 + n_samples = 0 + + for batch in loader: + smiles = batch["smiles"] + tabular = {k: v.to(device) for k, v in batch["tabular"].items()} + targets = batch["targets"]["delivery"].to(device) + mask = batch["mask"]["delivery"].to(device) + + pred = model.forward_delivery(smiles, tabular).squeeze(-1) + + if mask.any(): + loss = nn.functional.mse_loss(pred[mask], targets[mask]) + total_loss += loss.item() * mask.sum().item() + n_samples += mask.sum().item() + + avg_loss = total_loss / max(n_samples, 1) + return {"loss": avg_loss, "n_samples": n_samples} + + +def pretrain( + train_loader: DataLoader, + val_loader: DataLoader, + model: nn.Module, + device: torch.device, + lr: float = 1e-4, + weight_decay: float = 1e-5, + epochs: int = 50, + patience: int = 10, +) -> dict: + """ + 预训练循环。 + + Returns: + 训练历史和最佳验证损失 + """ + model = model.to(device) + trainable_params = [p for p in model.parameters() if p.requires_grad] + optimizer = torch.optim.AdamW(trainable_params, lr=lr, weight_decay=weight_decay) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, mode="min", factor=0.5, patience=5 + ) + early_stopping = EarlyStopping(patience=patience) + + history = {"train": [], "val": []} + best_val_loss = float("inf") + best_state = None + + for epoch in range(epochs): + # Train + train_metrics = train_epoch_delivery(model, train_loader, optimizer, device, epoch) + + # Validate + val_metrics = validate_delivery(model, val_loader, device) + + # Log + logger.info( + f"Epoch {epoch + 1}/{epochs} | " + f"Train Loss: {train_metrics['loss']:.4f} | " + f"Val Loss: {val_metrics['loss']:.4f}" + ) + + history["train"].append(train_metrics) + history["val"].append(val_metrics) + + # Learning rate scheduling + scheduler.step(val_metrics["loss"]) + + # Save best model + if val_metrics["loss"] < best_val_loss: + best_val_loss = val_metrics["loss"] + best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()} + logger.info(f" -> New best model (val_loss={best_val_loss:.4f})") + + # Early stopping + if early_stopping(val_metrics["loss"]): + logger.info(f"Early stopping at epoch {epoch + 1}") + break + + # Restore best model + if best_state is not None: + model.load_state_dict(best_state) + + return { + "history": history, + "best_val_loss": best_val_loss, + } + + +@app.command() +def main( + # 数据路径（已处理的 parquet 文件） + train_path: Path = PROCESSED_DATA_DIR / "train_pretrain.parquet", + val_path: Path = PROCESSED_DATA_DIR / "val_pretrain.parquet", + output_dir: Path = MODELS_DIR, + # 模型参数 + d_model: int = 256, + num_heads: int = 8, + n_attn_layers: int = 4, + set_transformer_block: str = "sab", # "sab" | "isab" + fusion_strategy: str = "attention", + head_hidden_dim: int = 128, + dropout: float = 0.1, + # MoE 参数（消融开关） + use_moe: bool = False, + moe_n_experts: int = 4, + moe_top_k: int = 2, + moe_expert_hidden_mult: int = 2, + moe_jitter_noise: float = 0.0, + # LLM 参数（消融开关） + use_llm: bool = False, + llm_model_path: str = DEFAULT_MOLT5_PATH, + llm_freeze: bool = True, + llm_use_lora: bool = False, + llm_lora_r: int = 8, + llm_lora_alpha: int = 16, + llm_lora_dropout: float = 0.05, + # MPNN 参数（可选） + use_mpnn: bool = False, # 启用 MPNN，自动从默认路径加载 ensemble + mpnn_checkpoint: Optional[str] = None, + mpnn_ensemble_paths: Optional[str] = None, + mpnn_device: str = "cpu", + # 训练参数 + batch_size: int = 64, + lr: float = 1e-4, + weight_decay: float = 1e-5, + epochs: int = 50, + patience: int = 10, + # 随机种子 + seed: int = 42, + # 设备 + device: str = "cuda" if torch.cuda.is_available() else "cpu", +): + """ + 在外部 LiON 数据上预训练 backbone + delivery head。 + + 需要先运行 `make data_pretrain` 生成 parquet 文件。 + + 使用 --use-mpnn 启用 MPNN encoder（自动从 models/mpnn/all_amine_split_for_LiON 加载）。 + + 产出: + - models/pretrain_delivery.pt: 包含 backbone + delivery head 权重 + - models/pretrain_history.json: 训练历史 + """ + set_global_seed(seed) + logger.info(f"Using device: {device} | seed: {seed}") + device_obj = torch.device(device) + + # 加载已处理的 parquet 文件 + logger.info(f"Loading train data from {train_path}") + train_df = pd.read_parquet(train_path) + train_dataset = ExternalDeliveryDataset(train_df) + + logger.info(f"Loading val data from {val_path}") + val_df = pd.read_parquet(val_path) + val_dataset = ExternalDeliveryDataset(val_df) + + logger.info(f"Train samples: {len(train_dataset)}, Val samples: {len(val_dataset)}") + + train_loader = DataLoader( + train_dataset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn + ) + val_loader = DataLoader( + val_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn + ) + + # 解析 MPNN 配置 + # 优先级：mpnn_checkpoint > mpnn_ensemble_paths > use_mpnn（自动查找） + ensemble_paths_list = None + if mpnn_ensemble_paths: + ensemble_paths_list = mpnn_ensemble_paths.split(",") + elif use_mpnn and mpnn_checkpoint is None: + # --use-mpnn 但没有指定具体路径，自动查找 + logger.info(f"Auto-detecting MPNN ensemble from {DEFAULT_MPNN_ENSEMBLE_DIR}") + ensemble_paths_list = find_mpnn_ensemble_paths() + logger.info(f"Found {len(ensemble_paths_list)} MPNN models") + + enable_mpnn = mpnn_checkpoint is not None or ensemble_paths_list is not None + + # 创建模型 + logger.info( + f"Creating model (use_mpnn={enable_mpnn}, use_moe={use_moe}, use_llm={use_llm})..." + ) + common_kwargs = dict( + d_model=d_model, + num_heads=num_heads, + n_attn_layers=n_attn_layers, + set_transformer_block=set_transformer_block, + fusion_strategy=fusion_strategy, + head_hidden_dim=head_hidden_dim, + dropout=dropout, + use_moe=use_moe, + moe_n_experts=moe_n_experts, + moe_top_k=moe_top_k, + moe_expert_hidden_mult=moe_expert_hidden_mult, + moe_jitter_noise=moe_jitter_noise, + use_llm=use_llm, + llm_model_path=llm_model_path, + llm_freeze=llm_freeze, + llm_use_lora=llm_use_lora, + llm_lora_r=llm_lora_r, + llm_lora_alpha=llm_lora_alpha, + llm_lora_dropout=llm_lora_dropout, + ) + if enable_mpnn: + model = LNPModel( + mpnn_checkpoint=mpnn_checkpoint, + mpnn_ensemble_paths=ensemble_paths_list, + mpnn_device=mpnn_device, + **common_kwargs, + ) + else: + model = LNPModelWithoutMPNN(**common_kwargs) + + n_params_total = sum(p.numel() for p in model.parameters()) + n_params_trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) + logger.info(f"Model parameters: {n_params_total:,} total, {n_params_trainable:,} trainable") + + # 预热 RDKit 缓存（避免训练时阻塞） + all_smiles = train_df["smiles"].tolist() + val_df["smiles"].tolist() + warmup_cache(model, all_smiles, batch_size=256) + + # 预训练 + logger.info("Starting pretraining on external data (delivery only)...") + result = pretrain( + train_loader=train_loader, + val_loader=val_loader, + model=model, + device=device_obj, + lr=lr, + weight_decay=weight_decay, + epochs=epochs, + patience=patience, + ) + + # 保存预训练 checkpoint + output_dir.mkdir(parents=True, exist_ok=True) + checkpoint_path = output_dir / "pretrain_delivery.pt" + torch.save( + { + "model_state_dict": model.state_dict(), + "backbone_state_dict": model.get_backbone_state_dict(), + "delivery_head_state_dict": model.get_delivery_head_state_dict(), + "config": { + "d_model": d_model, + "num_heads": num_heads, + "n_attn_layers": n_attn_layers, + "set_transformer_block": set_transformer_block, + "fusion_strategy": fusion_strategy, + "head_hidden_dim": head_hidden_dim, + "dropout": dropout, + "use_mpnn": enable_mpnn, + "use_moe": use_moe, + "moe_n_experts": moe_n_experts, + "moe_top_k": moe_top_k, + "moe_expert_hidden_mult": moe_expert_hidden_mult, + "moe_jitter_noise": moe_jitter_noise, + "use_llm": use_llm, + "llm_model_path": llm_model_path, + "llm_freeze": llm_freeze, + "llm_use_lora": llm_use_lora, + "llm_lora_r": llm_lora_r, + "llm_lora_alpha": llm_lora_alpha, + "llm_lora_dropout": llm_lora_dropout, + }, + "best_val_loss": result["best_val_loss"], + }, + checkpoint_path, + ) + logger.success(f"Saved pretrain checkpoint to {checkpoint_path}") + + # 保存训练历史 + history_path = output_dir / "pretrain_history.json" + with open(history_path, "w") as f: + json.dump(result["history"], f, indent=2) + logger.success(f"Saved pretrain history to {history_path}") + + # 绘制 loss 曲线图 + loss_plot_path = output_dir / "pretrain_loss_curves.png" + plot_loss_curves( + history=result["history"], + output_path=loss_plot_path, + title="Pretrain Loss Curves (Delivery)", + ) + logger.success(f"Saved loss curves plot to {loss_plot_path}") + + logger.success( + f"Pretraining complete! Best val_loss: {result['best_val_loss']:.4f}" + ) + + +@app.command() +def test( + # 数据路径 + val_path: Path = PROCESSED_DATA_DIR / "val_pretrain.parquet", + model_path: Path = MODELS_DIR / "pretrain_delivery.pt", + output_path: Path = MODELS_DIR / "pretrain_test_results.json", + # MPNN 参数 + use_mpnn: bool = False, + mpnn_device: str = "cpu", + # 其他参数 + batch_size: int = 64, + device: str = "cuda" if torch.cuda.is_available() else "cpu", +): + """ + 评估 pretrain 模型在外部数据上的 delivery 预测性能。 + + 输出详细指标：MSE, RMSE, MAE, R² + """ + import numpy as np + from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score + + logger.info(f"Using device: {device}") + device_obj = torch.device(device) + + # 加载模型 + logger.info(f"Loading pretrain model from {model_path}") + checkpoint = torch.load(model_path, map_location=device_obj, weights_only=False) + config = checkpoint["config"] + + # 解析 MPNN 配置 + enable_mpnn = config.get("use_mpnn", False) + common_kwargs = dict( + d_model=config["d_model"], + num_heads=config["num_heads"], + n_attn_layers=config["n_attn_layers"], + set_transformer_block=config.get("set_transformer_block", "sab"), + fusion_strategy=config["fusion_strategy"], + head_hidden_dim=config["head_hidden_dim"], + dropout=config["dropout"], + use_moe=config.get("use_moe", False), + moe_n_experts=config.get("moe_n_experts", 4), + moe_top_k=config.get("moe_top_k", 2), + moe_expert_hidden_mult=config.get("moe_expert_hidden_mult", 2), + moe_jitter_noise=config.get("moe_jitter_noise", 0.0), + use_llm=config.get("use_llm", False), + llm_model_path=config.get("llm_model_path", DEFAULT_MOLT5_PATH), + llm_freeze=config.get("llm_freeze", True), + llm_use_lora=config.get("llm_use_lora", False), + llm_lora_r=config.get("llm_lora_r", 8), + llm_lora_alpha=config.get("llm_lora_alpha", 16), + llm_lora_dropout=config.get("llm_lora_dropout", 0.05), + ) + if enable_mpnn or use_mpnn: + logger.info(f"Auto-detecting MPNN ensemble from {DEFAULT_MPNN_ENSEMBLE_DIR}") + ensemble_paths = find_mpnn_ensemble_paths() + logger.info(f"Found {len(ensemble_paths)} MPNN models") + model = LNPModel( + mpnn_ensemble_paths=ensemble_paths, + mpnn_device=mpnn_device, + **common_kwargs, + ) + else: + model = LNPModelWithoutMPNN(**common_kwargs) + + model.load_state_dict(checkpoint["model_state_dict"], strict=False) + model.to(device_obj) + model.eval() + + logger.info(f"Model config: {config}") + logger.info(f"Best val_loss from training: {checkpoint.get('best_val_loss', 'N/A')}") + + # 加载数据 + logger.info(f"Loading validation data from {val_path}") + val_df = pd.read_parquet(val_path) + val_dataset = ExternalDeliveryDataset(val_df) + val_loader = DataLoader( + val_dataset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn + ) + logger.info(f"Validation samples: {len(val_dataset)}") + + # 预测 + logger.info("Running predictions...") + all_preds = [] + all_targets = [] + all_masks = [] + + with torch.no_grad(): + for batch in tqdm(val_loader, desc="Predicting"): + smiles = batch["smiles"] + tabular = {k: v.to(device_obj) for k, v in batch["tabular"].items()} + targets = batch["targets"]["delivery"].numpy() + mask = batch["mask"]["delivery"].numpy() + + pred = model.forward_delivery(smiles, tabular).squeeze(-1).cpu().numpy() + + all_preds.extend(pred) + all_targets.extend(targets) + all_masks.extend(mask) + + # 转为数组 + all_preds = np.array(all_preds) + all_targets = np.array(all_targets) + all_masks = np.array(all_masks, dtype=bool) + + # 只计算有效样本 + y_pred = all_preds[all_masks] + y_true = all_targets[all_masks] + + # 计算指标 + mse = float(mean_squared_error(y_true, y_pred)) + rmse = float(np.sqrt(mse)) + mae = float(mean_absolute_error(y_true, y_pred)) + r2 = float(r2_score(y_true, y_pred)) + + # 额外统计 + correlation = float(np.corrcoef(y_true, y_pred)[0, 1]) + + results = { + "model_path": str(model_path), + "val_path": str(val_path), + "n_samples": int(all_masks.sum()), + "metrics": { + "mse": mse, + "rmse": rmse, + "mae": mae, + "r2": r2, + "correlation": correlation, + }, + "statistics": { + "y_true_mean": float(y_true.mean()), + "y_true_std": float(y_true.std()), + "y_pred_mean": float(y_pred.mean()), + "y_pred_std": float(y_pred.std()), + } + } + + # 打印结果 + logger.info("\n" + "=" * 50) + logger.info("PRETRAIN MODEL EVALUATION (Delivery)") + logger.info("=" * 50) + logger.info(f"Samples: {results['n_samples']}") + logger.info("\n[Metrics]") + logger.info(f" MSE: {mse:.4f}") + logger.info(f" RMSE: {rmse:.4f}") + logger.info(f" MAE: {mae:.4f}") + logger.info(f" R²: {r2:.4f}") + logger.info(f" Correlation: {correlation:.4f}") + logger.info("\n[Statistics]") + logger.info(f" True: mean={y_true.mean():.4f}, std={y_true.std():.4f}") + logger.info(f" Pred: mean={y_pred.mean():.4f}, std={y_pred.std():.4f}") + + # 保存结果 + with open(output_path, "w") as f: + json.dump(results, f, indent=2) + logger.success(f"\nSaved results to {output_path}") + + +if __name__ == "__main__": + app() + diff --git a/lnp_ml/modeling/trainer_balanced.py b/lnp_ml/modeling/trainer_balanced.py index 147cd5f..d873a4a 100644 --- a/lnp_ml/modeling/trainer_balanced.py +++ b/lnp_ml/modeling/trainer_balanced.py @@ -1,523 +1,551 @@ -"""带类权重的训练器：处理分类任务的数据不均衡问题""" - -from typing import Dict, List, Optional, Tuple -from dataclasses import dataclass, field - -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.utils.data import DataLoader -from tqdm import tqdm - - -@dataclass -class ClassWeights: - """分类任务的类权重""" - pdi: Optional[torch.Tensor] = None # [4] for 4 PDI classes - ee: Optional[torch.Tensor] = None # [3] for 3 EE classes - toxic: Optional[torch.Tensor] = None # [2] for binary toxic - - -@dataclass -class LossWeightsBalanced: - """各任务的损失权重（与 trainer.py 的 LossWeights 兼容）""" - size: float = 1.0 - pdi: float = 1.0 - ee: float = 1.0 - delivery: float = 1.0 - biodist: float = 1.0 - toxic: float = 1.0 - moe_lb: float = 0.01 # MoE load-balancing 系数（仅在 use_moe=True 时生效） - - -def compute_class_weights_from_loader( - loader: DataLoader, - n_pdi_classes: int = 4, - n_ee_classes: int = 3, - n_toxic_classes: int = 2, - smoothing: float = 0.1, -) -> ClassWeights: - """ - 从 DataLoader 统计类别频次并计算类权重。 - - 使用 inverse frequency 方式：weight_c = N / (n_classes * count_c) - 加 smoothing 避免极端权重。 - - Args: - loader: DataLoader（需要遍历一次） - n_pdi_classes: PDI 类别数 - n_ee_classes: EE 类别数 - n_toxic_classes: toxic 类别数 - smoothing: 平滑系数（防止除零和极端权重） - - Returns: - ClassWeights 对象，包含各分类任务的类权重张量 - """ - pdi_counts = torch.zeros(n_pdi_classes) - ee_counts = torch.zeros(n_ee_classes) - toxic_counts = torch.zeros(n_toxic_classes) - - for batch in loader: - targets = batch["targets"] - mask = batch["mask"] - - # PDI - if "pdi" in targets and "pdi" in mask: - m = mask["pdi"] - if m.any(): - labels = targets["pdi"][m] - for c in range(n_pdi_classes): - pdi_counts[c] += (labels == c).sum().item() - - # EE - if "ee" in targets and "ee" in mask: - m = mask["ee"] - if m.any(): - labels = targets["ee"][m] - for c in range(n_ee_classes): - ee_counts[c] += (labels == c).sum().item() - - # Toxic - if "toxic" in targets and "toxic" in mask: - m = mask["toxic"] - if m.any(): - labels = targets["toxic"][m] - for c in range(n_toxic_classes): - toxic_counts[c] += (labels == c).sum().item() - - def counts_to_weights(counts: torch.Tensor, n_classes: int) -> Optional[torch.Tensor]: - """将计数转换为类权重""" - total = counts.sum().item() - if total == 0: - return None - # Inverse frequency with smoothing - counts = counts + smoothing - weights = total / (n_classes * counts) - # Normalize to mean=1 - weights = weights / weights.mean() - return weights - - return ClassWeights( - pdi=counts_to_weights(pdi_counts, n_pdi_classes), - ee=counts_to_weights(ee_counts, n_ee_classes), - toxic=counts_to_weights(toxic_counts, n_toxic_classes), - ) - - -def compute_multitask_loss_balanced( - outputs: Dict[str, torch.Tensor], - targets: Dict[str, torch.Tensor], - mask: Dict[str, torch.Tensor], - task_weights: Optional[LossWeightsBalanced] = None, - class_weights: Optional[ClassWeights] = None, - model: Optional[nn.Module] = None, -) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: - """ - 计算带类权重的多任务损失。 - - Args: - outputs: 模型输出 - targets: 真实标签 - mask: 有效样本掩码 - task_weights: 各任务权重 - class_weights: 分类任务的类权重 - - Returns: - (total_loss, loss_dict) 总损失和各任务损失 - """ - task_weights = task_weights or LossWeightsBalanced() - class_weights = class_weights or ClassWeights() - - losses = {} - device = next(iter(outputs.values())).device - total_loss = torch.tensor(0.0, device=device) - - # size: MSE loss（回归任务，不需要类权重） - if "size" in targets and mask["size"].any(): - m = mask["size"] - pred = outputs["size"][m].squeeze(-1) - tgt = targets["size"][m] - losses["size"] = F.mse_loss(pred, tgt) - total_loss = total_loss + task_weights.size * losses["size"] - - # delivery: MSE loss（回归任务，不需要类权重） - if "delivery" in targets and mask["delivery"].any(): - m = mask["delivery"] - pred = outputs["delivery"][m].squeeze(-1) - tgt = targets["delivery"][m] - losses["delivery"] = F.mse_loss(pred, tgt) - total_loss = total_loss + task_weights.delivery * losses["delivery"] - - # pdi: CrossEntropy with class weights - if "pdi" in targets and mask["pdi"].any(): - m = mask["pdi"] - pred = outputs["pdi"][m] - tgt = targets["pdi"][m] - weight = class_weights.pdi.to(device) if class_weights.pdi is not None else None - losses["pdi"] = F.cross_entropy(pred, tgt, weight=weight) - total_loss = total_loss + task_weights.pdi * losses["pdi"] - - # ee: CrossEntropy with class weights - if "ee" in targets and mask["ee"].any(): - m = mask["ee"] - pred = outputs["ee"][m] - tgt = targets["ee"][m] - weight = class_weights.ee.to(device) if class_weights.ee is not None else None - losses["ee"] = F.cross_entropy(pred, tgt, weight=weight) - total_loss = total_loss + task_weights.ee * losses["ee"] - - # toxic: CrossEntropy with class weights - if "toxic" in targets and mask["toxic"].any(): - m = mask["toxic"] - pred = outputs["toxic"][m] - tgt = targets["toxic"][m] - weight = class_weights.toxic.to(device) if class_weights.toxic is not None else None - losses["toxic"] = F.cross_entropy(pred, tgt, weight=weight) - total_loss = total_loss + task_weights.toxic * losses["toxic"] - - # biodist: KL divergence（分布任务，不需要类权重） - if "biodist" in targets and mask["biodist"].any(): - m = mask["biodist"] - pred = outputs["biodist"][m] - tgt = targets["biodist"][m] - losses["biodist"] = F.kl_div( - pred.log().clamp(min=-100), - tgt, - reduction="batchmean", - ) - total_loss = total_loss + task_weights.biodist * losses["biodist"] - - # MoE load-balancing aux loss（可选） - if model is not None and hasattr(model, "get_last_moe_extras"): - extras = model.get_last_moe_extras() - if extras is not None and "lb_loss" in extras: - losses["moe_lb"] = extras["lb_loss"] - total_loss = total_loss + task_weights.moe_lb * losses["moe_lb"] - - return total_loss, losses - - -def train_epoch_balanced( - model: nn.Module, - loader: DataLoader, - optimizer: torch.optim.Optimizer, - device: torch.device, - task_weights: Optional[LossWeightsBalanced] = None, - class_weights: Optional[ClassWeights] = None, -) -> Dict[str, float]: - """带类权重的训练一个 epoch""" - model.train() - total_loss = 0.0 - task_losses = {k: 0.0 for k in ["size", "pdi", "ee", "delivery", "biodist", "toxic", "moe_lb"]} - n_batches = 0 - - for batch in tqdm(loader, desc="Training", leave=False): - smiles = batch["smiles"] - tabular = {k: v.to(device) for k, v in batch["tabular"].items()} - targets = {k: v.to(device) for k, v in batch["targets"].items()} - mask = {k: v.to(device) for k, v in batch["mask"].items()} - - optimizer.zero_grad() - outputs = model(smiles, tabular) - loss, losses = compute_multitask_loss_balanced( - outputs, targets, mask, task_weights, class_weights, model=model, - ) - - loss.backward() - torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) - optimizer.step() - - total_loss += loss.item() - for k, v in losses.items(): - task_losses[k] += v.item() - n_batches += 1 - - return { - "loss": total_loss / n_batches, - **{f"loss_{k}": v / n_batches for k, v in task_losses.items() if v > 0}, - } - - -@torch.no_grad() -def validate_balanced( - model: nn.Module, - loader: DataLoader, - device: torch.device, - task_weights: Optional[LossWeightsBalanced] = None, - class_weights: Optional[ClassWeights] = None, -) -> Dict[str, float]: - """带类权重的验证""" - model.eval() - total_loss = 0.0 - task_losses = {k: 0.0 for k in ["size", "pdi", "ee", "delivery", "biodist", "toxic", "moe_lb"]} - n_batches = 0 - - # 用于计算准确率 - correct = {k: 0 for k in ["pdi", "ee", "toxic"]} - total = {k: 0 for k in ["pdi", "ee", "toxic"]} - - for batch in tqdm(loader, desc="Validating", leave=False): - smiles = batch["smiles"] - tabular = {k: v.to(device) for k, v in batch["tabular"].items()} - targets = {k: v.to(device) for k, v in batch["targets"].items()} - mask = {k: v.to(device) for k, v in batch["mask"].items()} - - outputs = model(smiles, tabular) - loss, losses = compute_multitask_loss_balanced( - outputs, targets, mask, task_weights, class_weights, model=model, - ) - - total_loss += loss.item() - for k, v in losses.items(): - task_losses[k] += v.item() - n_batches += 1 - - # 计算分类准确率 - for k in ["pdi", "ee", "toxic"]: - if k in targets and mask[k].any(): - m = mask[k] - pred = outputs[k][m].argmax(dim=-1) - tgt = targets[k][m] - correct[k] += (pred == tgt).sum().item() - total[k] += m.sum().item() - - metrics = { - "loss": total_loss / n_batches, - **{f"loss_{k}": v / n_batches for k, v in task_losses.items() if v > 0}, - } - - # 添加准确率 - for k in ["pdi", "ee", "toxic"]: - if total[k] > 0: - metrics[f"acc_{k}"] = correct[k] / total[k] - - return metrics - - -BACKBONE_PREFIXES = ("token_projector.", "cross_attention.", "fusion.", "moe.") - - -def build_optimizer( - model: nn.Module, - lr: float, - weight_decay: float, - backbone_lr_ratio: float = 1.0, -) -> torch.optim.AdamW: - """ - 构建 AdamW 优化器，支持分层学习率。 - - 当 backbone_lr_ratio < 1.0 时，backbone 参数使用 lr * backbone_lr_ratio， - 其余参数（task heads 等）使用 lr。backbone_lr_ratio = 1.0 等价于统一学习率。 - """ - if backbone_lr_ratio >= 1.0: - return torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay) - - backbone_params = [] - head_params = [] - for name, param in model.named_parameters(): - if not param.requires_grad: - continue - if name.startswith(BACKBONE_PREFIXES): - backbone_params.append(param) - else: - head_params.append(param) - - return torch.optim.AdamW( - [ - {"params": backbone_params, "lr": lr * backbone_lr_ratio}, - {"params": head_params, "lr": lr}, - ], - weight_decay=weight_decay, - ) - - -class EarlyStoppingBalanced: - """早停机制（与 trainer.py 的 EarlyStopping 兼容）""" - - def __init__(self, patience: int = 10, min_delta: float = 0.0): - self.patience = patience - self.min_delta = min_delta - self.counter = 0 - self.best_loss = float("inf") - self.best_epoch = 0 - self.should_stop = False - - def __call__(self, val_loss: float, epoch: int = 0) -> bool: - if val_loss < self.best_loss - self.min_delta: - self.best_loss = val_loss - self.best_epoch = epoch - self.counter = 0 - else: - self.counter += 1 - if self.counter >= self.patience: - self.should_stop = True - return self.should_stop - - def get_best_epoch(self) -> int: - """获取最佳 epoch（1-indexed）""" - return self.best_epoch + 1 - - -def train_with_early_stopping( - model: nn.Module, - train_loader: DataLoader, - val_loader: DataLoader, - device: torch.device, - lr: float = 1e-4, - weight_decay: float = 1e-5, - epochs: int = 100, - patience: int = 15, - task_weights: Optional[LossWeightsBalanced] = None, - class_weights: Optional[ClassWeights] = None, - backbone_lr_ratio: float = 1.0, -) -> Dict: - """ - 带早停的完整训练流程。 - - Returns: - Dict with keys: history, best_val_loss, best_epoch, best_state - """ - model = model.to(device) - optimizer = build_optimizer(model, lr, weight_decay, backbone_lr_ratio) - scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, mode="min", factor=0.5, patience=5 - ) - early_stopping = EarlyStoppingBalanced(patience=patience) - - history = {"train": [], "val": []} - best_val_loss = float("inf") - best_state = None - - for epoch in range(epochs): - # Train - train_metrics = train_epoch_balanced( - model, train_loader, optimizer, device, task_weights, class_weights - ) - - # Validate - val_metrics = validate_balanced( - model, val_loader, device, task_weights, class_weights - ) - - history["train"].append(train_metrics) - history["val"].append(val_metrics) - - # Learning rate scheduling - scheduler.step(val_metrics["loss"]) - - # Save best model - if val_metrics["loss"] < best_val_loss: - best_val_loss = val_metrics["loss"] - best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()} - - # Early stopping - if early_stopping(val_metrics["loss"], epoch): - break - - # Restore best model - if best_state is not None: - model.load_state_dict(best_state) - - return { - "history": history, - "best_val_loss": best_val_loss, - "best_epoch": early_stopping.get_best_epoch(), - "best_state": best_state, - "epochs_trained": len(history["train"]), - } - - -def train_fixed_epochs( - model: nn.Module, - train_loader: DataLoader, - val_loader: Optional[DataLoader], - device: torch.device, - lr: float = 1e-4, - weight_decay: float = 1e-5, - epochs: int = 50, - task_weights: Optional[LossWeightsBalanced] = None, - class_weights: Optional[ClassWeights] = None, - use_cosine_annealing: bool = True, - use_swa: bool = False, - swa_start_epoch: Optional[int] = None, - backbone_lr_ratio: float = 1.0, -) -> Dict: - """ - 固定 epoch 数的训练（不使用 early stopping）。 - - 用于外层 CV 训练和最终训练。 - - Args: - model: 模型 - train_loader: 训练数据 - val_loader: 验证数据（可选，仅用于监控） - device: 设备 - lr: 学习率 - weight_decay: 权重衰减 - epochs: 训练轮数 - task_weights: 任务权重 - class_weights: 类权重 - use_cosine_annealing: 是否使用 CosineAnnealingLR - use_swa: 是否使用 SWA - swa_start_epoch: SWA 开始的 epoch（默认为 epochs * 0.75） - backbone_lr_ratio: backbone 学习率相对于 head 的比例（1.0 = 统一学习率） - - Returns: - Dict with keys: history, final_state - """ - model = model.to(device) - optimizer = build_optimizer(model, lr, weight_decay, backbone_lr_ratio) - - if use_cosine_annealing: - scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs) - else: - scheduler = None - - # SWA setup - swa_model = None - swa_scheduler = None - if use_swa: - from torch.optim.swa_utils import AveragedModel, SWALR - swa_model = AveragedModel(model) - swa_start = swa_start_epoch or int(epochs * 0.75) - swa_scheduler = SWALR(optimizer, swa_lr=lr * 0.1) - - history = {"train": [], "val": []} - - for epoch in range(epochs): - # Train - train_metrics = train_epoch_balanced( - model, train_loader, optimizer, device, task_weights, class_weights - ) - history["train"].append(train_metrics) - - # Validate (optional) - if val_loader is not None: - val_metrics = validate_balanced( - model, val_loader, device, task_weights, class_weights - ) - history["val"].append(val_metrics) - - # Scheduler step - if use_swa and epoch >= swa_start: - swa_model.update_parameters(model) - swa_scheduler.step() - elif scheduler is not None: - scheduler.step() - - # Finalize SWA - final_state = None - if use_swa and swa_model is not None: - # Update batch normalization statistics - torch.optim.swa_utils.update_bn(train_loader, swa_model, device=device) - final_state = {k: v.cpu().clone() for k, v in swa_model.module.state_dict().items()} - else: - final_state = {k: v.cpu().clone() for k, v in model.state_dict().items()} - - return { - "history": history, - "final_state": final_state, - "epochs_trained": epochs, - } - +"""带类权重的训练器：处理分类任务的数据不均衡问题""" + +from typing import Dict, List, Optional, Tuple +from dataclasses import dataclass, field + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.utils.data import DataLoader +from tqdm import tqdm + + +@dataclass +class ClassWeights: + """分类任务的类权重""" + pdi: Optional[torch.Tensor] = None # [4] for 4 PDI classes + ee: Optional[torch.Tensor] = None # [3] for 3 EE classes + toxic: Optional[torch.Tensor] = None # [2] for binary toxic + + +@dataclass +class LossWeightsBalanced: + """各任务的损失权重（与 trainer.py 的 LossWeights 兼容）""" + size: float = 1.0 + pdi: float = 1.0 + ee: float = 1.0 + delivery: float = 1.0 + biodist: float = 1.0 + toxic: float = 1.0 + moe_lb: float = 0.01 # MoE load-balancing 系数（仅在 use_moe=True 时生效） + moe_lb: float = 0.01 # MoE load-balancing 系数（仅在 use_moe=True 时生效） + + +def compute_class_weights_from_loader( + loader: DataLoader, + n_pdi_classes: int = 4, + n_ee_classes: int = 3, + n_toxic_classes: int = 2, + smoothing: float = 0.1, +) -> ClassWeights: + """ + 从 DataLoader 统计类别频次并计算类权重。 + + 使用 inverse frequency 方式：weight_c = N / (n_classes * count_c) + 加 smoothing 避免极端权重。 + + Args: + loader: DataLoader（需要遍历一次） + n_pdi_classes: PDI 类别数 + n_ee_classes: EE 类别数 + n_toxic_classes: toxic 类别数 + smoothing: 平滑系数（防止除零和极端权重） + + Returns: + ClassWeights 对象，包含各分类任务的类权重张量 + """ + pdi_counts = torch.zeros(n_pdi_classes) + ee_counts = torch.zeros(n_ee_classes) + toxic_counts = torch.zeros(n_toxic_classes) + + for batch in loader: + targets = batch["targets"] + mask = batch["mask"] + + # PDI + if "pdi" in targets and "pdi" in mask: + m = mask["pdi"] + if m.any(): + labels = targets["pdi"][m] + for c in range(n_pdi_classes): + pdi_counts[c] += (labels == c).sum().item() + + # EE + if "ee" in targets and "ee" in mask: + m = mask["ee"] + if m.any(): + labels = targets["ee"][m] + for c in range(n_ee_classes): + ee_counts[c] += (labels == c).sum().item() + + # Toxic + if "toxic" in targets and "toxic" in mask: + m = mask["toxic"] + if m.any(): + labels = targets["toxic"][m] + for c in range(n_toxic_classes): + toxic_counts[c] += (labels == c).sum().item() + + def counts_to_weights(counts: torch.Tensor, n_classes: int) -> Optional[torch.Tensor]: + """将计数转换为类权重""" + total = counts.sum().item() + if total == 0: + return None + # Inverse frequency with smoothing + counts = counts + smoothing + weights = total / (n_classes * counts) + # Normalize to mean=1 + weights = weights / weights.mean() + return weights + + return ClassWeights( + pdi=counts_to_weights(pdi_counts, n_pdi_classes), + ee=counts_to_weights(ee_counts, n_ee_classes), + toxic=counts_to_weights(toxic_counts, n_toxic_classes), + ) + + +def compute_multitask_loss_balanced( + outputs: Dict[str, torch.Tensor], + targets: Dict[str, torch.Tensor], + mask: Dict[str, torch.Tensor], + task_weights: Optional[LossWeightsBalanced] = None, + class_weights: Optional[ClassWeights] = None, + model: Optional[nn.Module] = None, + model: Optional[nn.Module] = None, +) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]: + """ + 计算带类权重的多任务损失。 + + Args: + outputs: 模型输出 + targets: 真实标签 + mask: 有效样本掩码 + task_weights: 各任务权重 + class_weights: 分类任务的类权重 + + Returns: + (total_loss, loss_dict) 总损失和各任务损失 + """ + task_weights = task_weights or LossWeightsBalanced() + class_weights = class_weights or ClassWeights() + + # 若模型 head 暴露 log_vars，则启用不确定性加权（自动平衡），否则回退到标量权重 + log_vars = None + if model is not None and hasattr(model, "head") and hasattr(model.head, "log_vars"): + log_vars = model.head.log_vars + + losses = {} + device = next(iter(outputs.values())).device + total_loss = torch.tensor(0.0, device=device) + + def _add(name: str, raw_loss: torch.Tensor, scalar_w: float) -> None: + nonlocal total_loss + losses[name] = raw_loss + if log_vars is not None and name in log_vars: + s = log_vars[name] + total_loss = total_loss + torch.exp(-s) * raw_loss + 0.5 * s + else: + total_loss = total_loss + scalar_w * raw_loss + + # size: MSE + if "size" in targets and mask["size"].any(): + m = mask["size"] + _add("size", F.mse_loss(outputs["size"][m].squeeze(-1), targets["size"][m]), task_weights.size) + + # delivery: MSE + if "delivery" in targets and mask["delivery"].any(): + m = mask["delivery"] + _add("delivery", F.mse_loss(outputs["delivery"][m].squeeze(-1), targets["delivery"][m]), task_weights.delivery) + + # pdi: 类加权 CE + if "pdi" in targets and mask["pdi"].any(): + m = mask["pdi"] + w = class_weights.pdi.to(device) if class_weights.pdi is not None else None + _add("pdi", F.cross_entropy(outputs["pdi"][m], targets["pdi"][m], weight=w), task_weights.pdi) + + # ee: 类加权 CE + if "ee" in targets and mask["ee"].any(): + m = mask["ee"] + w = class_weights.ee.to(device) if class_weights.ee is not None else None + _add("ee", F.cross_entropy(outputs["ee"][m], targets["ee"][m], weight=w), task_weights.ee) + + # toxic: 类加权 CE + if "toxic" in targets and mask["toxic"].any(): + m = mask["toxic"] + w = class_weights.toxic.to(device) if class_weights.toxic is not None else None + _add("toxic", F.cross_entropy(outputs["toxic"][m], targets["toxic"][m], weight=w), task_weights.toxic) + + # biodist: KL + if "biodist" in targets and mask["biodist"].any(): + m = mask["biodist"] + kl = F.kl_div(outputs["biodist"][m].log().clamp(min=-100), targets["biodist"][m], reduction="batchmean") + _add("biodist", kl, task_weights.biodist) + + # MoE load-balancing aux loss（固定小权重，不纳入不确定性加权） + if model is not None and hasattr(model, "get_last_moe_extras"): + extras = model.get_last_moe_extras() + if extras is not None and "lb_loss" in extras: + losses["moe_lb"] = extras["lb_loss"] + total_loss = total_loss + task_weights.moe_lb * losses["moe_lb"] + + return total_loss, losses + + +def train_epoch_balanced( + model: nn.Module, + loader: DataLoader, + optimizer: torch.optim.Optimizer, + device: torch.device, + task_weights: Optional[LossWeightsBalanced] = None, + class_weights: Optional[ClassWeights] = None, +) -> Dict[str, float]: + """带类权重的训练一个 epoch""" + model.train() + total_loss = 0.0 + task_losses = {k: 0.0 for k in ["size", "pdi", "ee", "delivery", "biodist", "toxic", "moe_lb"]} + task_losses = {k: 0.0 for k in ["size", "pdi", "ee", "delivery", "biodist", "toxic", "moe_lb"]} + n_batches = 0 + + for batch in tqdm(loader, desc="Training", leave=False): + smiles = batch["smiles"] + tabular = {k: v.to(device) for k, v in batch["tabular"].items()} + targets = {k: v.to(device) for k, v in batch["targets"].items()} + mask = {k: v.to(device) for k, v in batch["mask"].items()} + + optimizer.zero_grad() + outputs = model(smiles, tabular) + loss, losses = compute_multitask_loss_balanced( + outputs, targets, mask, task_weights, class_weights, model=model, + outputs, targets, mask, task_weights, class_weights, model=model, + ) + + loss.backward() + torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) + optimizer.step() + + total_loss += loss.item() + for k, v in losses.items(): + task_losses[k] += v.item() + n_batches += 1 + + return { + "loss": total_loss / n_batches, + **{f"loss_{k}": v / n_batches for k, v in task_losses.items() if v > 0}, + } + + +@torch.no_grad() +def validate_balanced( + model: nn.Module, + loader: DataLoader, + device: torch.device, + task_weights: Optional[LossWeightsBalanced] = None, + class_weights: Optional[ClassWeights] = None, +) -> Dict[str, float]: + """带类权重的验证""" + model.eval() + total_loss = 0.0 + task_losses = {k: 0.0 for k in ["size", "pdi", "ee", "delivery", "biodist", "toxic", "moe_lb"]} + task_losses = {k: 0.0 for k in ["size", "pdi", "ee", "delivery", "biodist", "toxic", "moe_lb"]} + n_batches = 0 + + # 用于计算准确率 + correct = {k: 0 for k in ["pdi", "ee", "toxic"]} + total = {k: 0 for k in ["pdi", "ee", "toxic"]} + + for batch in tqdm(loader, desc="Validating", leave=False): + smiles = batch["smiles"] + tabular = {k: v.to(device) for k, v in batch["tabular"].items()} + targets = {k: v.to(device) for k, v in batch["targets"].items()} + mask = {k: v.to(device) for k, v in batch["mask"].items()} + + outputs = model(smiles, tabular) + loss, losses = compute_multitask_loss_balanced( + outputs, targets, mask, task_weights, class_weights, model=model, + outputs, targets, mask, task_weights, class_weights, model=model, + ) + + total_loss += loss.item() + for k, v in losses.items(): + task_losses[k] += v.item() + n_batches += 1 + + # 计算分类准确率 + for k in ["pdi", "ee", "toxic"]: + if k in targets and mask[k].any(): + m = mask[k] + pred = outputs[k][m].argmax(dim=-1) + tgt = targets[k][m] + correct[k] += (pred == tgt).sum().item() + total[k] += m.sum().item() + + metrics = { + "loss": total_loss / n_batches, + **{f"loss_{k}": v / n_batches for k, v in task_losses.items() if v > 0}, + } + + # 添加准确率 + for k in ["pdi", "ee", "toxic"]: + if total[k] > 0: + metrics[f"acc_{k}"] = correct[k] / total[k] + + return metrics + + +BACKBONE_PREFIXES = ( + "token_projector.", + "set_transformer.", + "fusion.", + "moe.", + "llm_prompt.", +) + +FROM_SCRATCH_PREFIXES = ( + "moe.", + "llm_prompt.", + "fusion.g_moe", + "fusion.g_llm", +) + + +def build_optimizer( + model: nn.Module, + lr: float, + weight_decay: float, + backbone_lr_ratio: float = 1.0, +) -> torch.optim.AdamW: + """ + 构建 AdamW 优化器，支持分层学习率。 + + 仅收集 requires_grad=True 的参数（冻结的 MolT5 encoder 被排除）。 + 当 backbone_lr_ratio < 1.0 时，backbone 参数使用 lr * backbone_lr_ratio， + 其余参数（task heads 等）使用 lr。backbone_lr_ratio = 1.0 等价于统一学习率。 + """ + trainable = [(n, p) for n, p in model.named_parameters() if p.requires_grad] + + if backbone_lr_ratio >= 1.0: + return torch.optim.AdamW( + [p for _, p in trainable], lr=lr, weight_decay=weight_decay + ) + + backbone_params = [] + head_params = [] + for name, param in trainable: + if name.startswith(BACKBONE_PREFIXES) and not name.startswith(FROM_SCRATCH_PREFIXES): + backbone_params.append(param) + else: + head_params.append(param) + + return torch.optim.AdamW( + [ + {"params": backbone_params, "lr": lr * backbone_lr_ratio}, + {"params": head_params, "lr": lr}, + ], + weight_decay=weight_decay, + ) + + +class EarlyStoppingBalanced: + """早停机制（与 trainer.py 的 EarlyStopping 兼容）""" + + def __init__(self, patience: int = 10, min_delta: float = 0.0): + self.patience = patience + self.min_delta = min_delta + self.counter = 0 + self.best_loss = float("inf") + self.best_epoch = 0 + self.should_stop = False + + def __call__(self, val_loss: float, epoch: int = 0) -> bool: + if val_loss < self.best_loss - self.min_delta: + self.best_loss = val_loss + self.best_epoch = epoch + self.counter = 0 + else: + self.counter += 1 + if self.counter >= self.patience: + self.should_stop = True + return self.should_stop + + def get_best_epoch(self) -> int: + """获取最佳 epoch（1-indexed）""" + return self.best_epoch + 1 + + +def train_with_early_stopping( + model: nn.Module, + train_loader: DataLoader, + val_loader: DataLoader, + device: torch.device, + lr: float = 1e-4, + weight_decay: float = 1e-5, + epochs: int = 100, + patience: int = 15, + task_weights: Optional[LossWeightsBalanced] = None, + class_weights: Optional[ClassWeights] = None, + backbone_lr_ratio: float = 1.0, +) -> Dict: + """ + 带早停的完整训练流程。 + + Returns: + Dict with keys: history, best_val_loss, best_epoch, best_state + """ + model = model.to(device) + optimizer = build_optimizer(model, lr, weight_decay, backbone_lr_ratio) + scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + optimizer, mode="min", factor=0.5, patience=5 + ) + early_stopping = EarlyStoppingBalanced(patience=patience) + + history = {"train": [], "val": []} + best_val_loss = float("inf") + best_state = None + + for epoch in range(epochs): + # Train + train_metrics = train_epoch_balanced( + model, train_loader, optimizer, device, task_weights, class_weights + ) + + # Validate + val_metrics = validate_balanced( + model, val_loader, device, task_weights, class_weights + ) + + history["train"].append(train_metrics) + history["val"].append(val_metrics) + + # Learning rate scheduling + scheduler.step(val_metrics["loss"]) + + # Save best model + if val_metrics["loss"] < best_val_loss: + best_val_loss = val_metrics["loss"] + best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()} + + # Early stopping + if early_stopping(val_metrics["loss"], epoch): + break + + # Restore best model + if best_state is not None: + model.load_state_dict(best_state) + + return { + "history": history, + "best_val_loss": best_val_loss, + "best_epoch": early_stopping.get_best_epoch(), + "best_state": best_state, + "epochs_trained": len(history["train"]), + } + + +def train_fixed_epochs( + model: nn.Module, + train_loader: DataLoader, + val_loader: Optional[DataLoader], + device: torch.device, + lr: float = 1e-4, + weight_decay: float = 1e-5, + epochs: int = 50, + task_weights: Optional[LossWeightsBalanced] = None, + class_weights: Optional[ClassWeights] = None, + use_cosine_annealing: bool = True, + use_swa: bool = False, + swa_start_epoch: Optional[int] = None, + backbone_lr_ratio: float = 1.0, + freeze_backbone_epochs: int = 0, +) -> Dict: + """ + 固定 epoch 数的训练（不使用 early stopping）。 + + 用于外层 CV 训练和最终训练。 + + Args: + model: 模型 + train_loader: 训练数据 + val_loader: 验证数据（可选，仅用于监控） + device: 设备 + lr: 学习率 + weight_decay: 权重衰减 + epochs: 训练轮数 + task_weights: 任务权重 + class_weights: 类权重 + use_cosine_annealing: 是否使用 CosineAnnealingLR + use_swa: 是否使用 SWA + swa_start_epoch: SWA 开始的 epoch（默认为 epochs * 0.75） + backbone_lr_ratio: backbone 学习率相对于 head 的比例（1.0 = 统一学习率） + + Returns: + Dict with keys: history, final_state + """ + model = model.to(device) + optimizer = build_optimizer(model, lr, weight_decay, backbone_lr_ratio) + + backbone_named = [ + (n, p) for n, p in model.named_parameters() + if n.startswith(BACKBONE_PREFIXES) and not n.startswith(FROM_SCRATCH_PREFIXES) + and p.requires_grad + ] + if freeze_backbone_epochs > 0: + for _, p in backbone_named: + p.requires_grad_(False) + + if use_cosine_annealing: + scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs) + else: + scheduler = None + + # SWA setup + swa_model = None + swa_scheduler = None + if use_swa: + from torch.optim.swa_utils import AveragedModel, SWALR + swa_model = AveragedModel(model) + swa_start = swa_start_epoch or int(epochs * 0.75) + swa_scheduler = SWALR(optimizer, swa_lr=lr * 0.1) + + history = {"train": [], "val": []} + + for epoch in range(epochs): + if freeze_backbone_epochs > 0 and epoch == freeze_backbone_epochs: + for _, p in backbone_named: + p.requires_grad_(True) + # Train + train_metrics = train_epoch_balanced( + model, train_loader, optimizer, device, task_weights, class_weights + ) + history["train"].append(train_metrics) + + # Validate (optional) + if val_loader is not None: + val_metrics = validate_balanced( + model, val_loader, device, task_weights, class_weights + ) + history["val"].append(val_metrics) + + # Scheduler step + if use_swa and epoch >= swa_start: + swa_model.update_parameters(model) + swa_scheduler.step() + elif scheduler is not None: + scheduler.step() + + # Finalize SWA + final_state = None + if use_swa and swa_model is not None: + # Update batch normalization statistics + torch.optim.swa_utils.update_bn(train_loader, swa_model, device=device) + final_state = {k: v.cpu().clone() for k, v in swa_model.module.state_dict().items()} + else: + final_state = {k: v.cpu().clone() for k, v in model.state_dict().items()} + + return { + "history": history, + "final_state": final_state, + "epochs_trained": epochs, + } + diff --git a/lnp_ml/utils/seed.py b/lnp_ml/utils/seed.py new file mode 100644 index 0000000..fd7f199 --- /dev/null +++ b/lnp_ml/utils/seed.py @@ -0,0 +1,19 @@ +"""全局随机种子工具。""" + +import random + +import numpy as np +import torch + + +def set_global_seed(seed: int) -> None: + """固定 random / numpy / torch 种子，提升可复现性。 + + 不开启 cudnn deterministic，以免显著拖慢训练；如需严格复现可自行追加： + torch.backends.cudnn.deterministic = True + torch.backends.cudnn.benchmark = False + """ + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) \ No newline at end of file diff --git a/models/abl/baseline/20260617_180556/outer_fold_0/best_params.json b/models/abl/baseline/20260617_180556/outer_fold_0/best_params.json new file mode 100644 index 0000000..3e2c1aa --- /dev/null +++ b/models/abl/baseline/20260617_180556/outer_fold_0/best_params.json @@ -0,0 +1,12 @@ +{ + "dropout": 0.28242799368681437, + "lr": 0.00037183641805732076, + "weight_decay": 6.290644294586152e-05, + "backbone_lr_ratio": 0.10677482709481352, + "d_model": 256, + "num_heads": 8, + "n_attn_layers": 4, + "fusion_strategy": "attention", + "head_hidden_dim": 128, + "set_transformer_block": "sab" +} \ No newline at end of file diff --git a/models/abl/baseline/20260617_180556/outer_fold_0/splits.json b/models/abl/baseline/20260617_180556/outer_fold_0/splits.json new file mode 100644 index 0000000..cccb9d8 --- /dev/null +++ b/models/abl/baseline/20260617_180556/outer_fold_0/splits.json @@ -0,0 +1 @@ +{"outer_train_idx": [0, 1, 2, 3, 4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 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<18" -fastparquet = ">=2024.2.0, <2025" -fastapi = ">=0.124.4, <0.125" -streamlit = ">=1.40.1, <2" -httpx = ">=0.28.1, <0.29" -uvicorn = ">=0.33.0, <0.34" -optuna = ">=4.5.0, <5" -captum = ">=0.7.0, <0.8" +[workspace] +authors = ["RYDE-WORK "] +channels = ["conda-forge"] +name = "LNP_ML" +platforms = ["linux-64", "osx-arm64"] +version = "0.1.0" + +[tasks] + +[dependencies] +loguru = "*" +ruff = "*" +tqdm = "*" +typer = "*" +pip = "*" +python = "3.8.*" + +[pypi-dependencies] +lnp_ml = { path = ".", editable = true } +chemprop = "==1.7.0" +setuptools = "*" +pandas = ">=2.0.3, <3" +openpyxl = ">=3.1.5, <4" +python-dotenv = ">=1.0.1, <2" +pyarrow = ">=17.0.0, <18" +fastparquet = ">=2024.2.0, <2025" +fastapi = ">=0.124.4, <0.125" +streamlit = ">=1.40.1, <2" +httpx = ">=0.28.1, <0.29" +uvicorn = ">=0.33.0, <0.34" +optuna = ">=4.5.0, <5" +captum = ">=0.7.0, <0.8" +transformers = ">=4.30, <4.46" +sentencepiece = "*" +protobuf = "*" diff --git a/pyproject.toml b/pyproject.toml index 37f2854..86b7890 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,32 +1,32 @@ -[build-system] -requires = ["flit_core >=3.2,<4"] -build-backend = "flit_core.buildapi" - -[project] -name = "lnp_ml" -version = "0.0.1" -description = "A short description of the project." -authors = [ - { name = "Wu Dinghong" }, -] -license = { file = "LICENSE" } -readme = "README.md" -classifiers = [ - "Programming Language :: Python :: 3", - "License :: OSI Approved :: MIT License" -] -requires-python = ">=3.8" - - -[tool.ruff] -line-length = 99 -src = ["lnp_ml"] -include = ["pyproject.toml", "lnp_ml/**/*.py"] - -[tool.ruff.lint] -extend-select = ["I"] # Add import sorting - -[tool.ruff.lint.isort] -known-first-party = ["lnp_ml"] -force-sort-within-sections = true - +[build-system] +requires = ["flit_core >=3.2,<4"] +build-backend = "flit_core.buildapi" + +[project] +name = "lnp_ml" +version = "0.0.1" +description = "A short description of the project." +authors = [ + { name = "Wu Dinghong" }, +] +license = { file = "LICENSE" } +readme = "README.md" +classifiers = [ + "Programming Language :: Python :: 3", + "License :: OSI Approved :: MIT License" +] +requires-python = ">=3.8" + + +[tool.ruff] +line-length = 99 +src = ["lnp_ml"] +include = ["pyproject.toml", "lnp_ml/**/*.py"] + +[tool.ruff.lint] +extend-select = ["I"] # Add import sorting + +[tool.ruff.lint.isort] +known-first-party = ["lnp_ml"] +force-sort-within-sections = true + diff --git a/reports/ablation_summary.csv b/reports/ablation_summary.csv new file mode 100644 index 0000000..ff52ed6 --- /dev/null +++ b/reports/ablation_summary.csv @@ -0,0 +1,5 @@ +variant,run_dir,size.mse_mean,size.mse_std,size.rmse_mean,size.rmse_std,size.mae_mean,size.mae_std,size.r2_mean,size.r2_std,delivery.mse_mean,delivery.mse_std,delivery.rmse_mean,delivery.rmse_std,delivery.mae_mean,delivery.mae_std,delivery.r2_mean,delivery.r2_std,pdi.accuracy_mean,pdi.accuracy_std,pdi.precision_mean,pdi.precision_std,pdi.recall_mean,pdi.recall_std,pdi.f1_mean,pdi.f1_std,ee.accuracy_mean,ee.accuracy_std,ee.precision_mean,ee.precision_std,ee.recall_mean,ee.recall_std,ee.f1_mean,ee.f1_std,toxic.accuracy_mean,toxic.accuracy_std,toxic.precision_mean,toxic.precision_std,toxic.recall_mean,toxic.recall_std,toxic.f1_mean,toxic.f1_std,biodist.kl_divergence_mean,biodist.kl_divergence_std,biodist.js_divergence_mean,biodist.js_divergence_std +baseline,models/abl/baseline/20260617_180556,0.2768235036159284,0.1346583260723207,0.508798491275714,0.13396864891266763,0.3427187344475763,0.06271329639776796,-0.9110994850991861,1.3609378589681,0.834075853950265,0.13581578656540713,0.9102708371721865,0.0740463162089242,0.634870878942305,0.04810398332825185,0.14309063048291837,0.06678544282427759,0.573809523809524,0.07315376902732006,0.40644301832628565,0.06081224559746048,0.591894166836878,0.15212645465643837,0.40503518503422653,0.07270554354315185,0.65,0.05460640448180816,0.5988843624524457,0.055997857124168736,0.6355952320322067,0.07902933872282923,0.5997699900861396,0.06505269720858664,0.9500396342534485,0.014290090778642328,0.7457142857142857,0.03149343955006944,0.9737706334802525,0.007575117962473199,0.81483431023254,0.0312513648447909,0.42024315728866957,0.04170766696370389,0.10843433388487847,0.010645869413863087 ++moe,models/abl/moe/20260617_191948,0.23034984029037256,0.09049026510650632,0.46822986556281965,0.105406988788203,0.32189987007849846,0.08014105320611228,-0.4728185006647179,0.5427422370821634,0.8054494222584121,0.16018870426337528,0.8929394867547544,0.09004718347937392,0.6145264492845487,0.045144810651025415,0.17842704120419808,0.06422601059093863,0.5896825396825397,0.05682229307831347,0.35469393906374463,0.055592769200553505,0.522735863838455,0.15050332076418682,0.3604048512722805,0.06427235489398077,0.6547619047619047,0.05481364015819684,0.6019542483742392,0.06379027980318479,0.6352859185632295,0.08160957226119422,0.6021181253673275,0.06960400235208303,0.9500779484296937,0.017755203225104002,0.7600104427736006,0.07046117058026663,0.9577096476824245,0.05611117888622105,0.813010272994035,0.03449626596837632,0.3520444477507348,0.08432681015154675,0.08975023050906776,0.021944673828634763 ++llm,models/abl/llm/20260617_203747,0.22339749912455512,0.13276860667147747,0.45253463978969516,0.13641810332564366,0.3048570797351743,0.07637999745755018,-0.2662118521181242,0.20796761595726418,0.810584411488471,0.17918852622038825,0.8948853549265088,0.09881605652183918,0.6158850481512493,0.04133793584599054,0.1761904116252725,0.0670662068919268,0.6,0.07271331378442132,0.382198316306454,0.059482007465697324,0.552886246650569,0.15151126540989962,0.3854764962481809,0.06295617241236845,0.6492063492063492,0.05989829290194668,0.5992000446776198,0.0738385570229797,0.6206719076130841,0.08494291371338275,0.5966089895417119,0.07618779152357238,0.9511890595408049,0.016071213375949578,0.7617961570593149,0.0689235340767993,0.958294443004062,0.05613748087593979,0.8151453855878632,0.03136647604932653,0.4240504874533223,0.049093469886439346,0.1077911642566912,0.015306583731384354 ++both,models/abl/both/20260617_232841,0.21249719869892153,0.083819203886272,0.4509291694638765,0.09570832161071456,0.29299021591121593,0.04711634555603193,-0.33611237791230353,0.3452040990881361,0.7425693610280117,0.15313439940788104,0.8571056675991812,0.08910238828097625,0.5779881956006352,0.040317114677088234,0.24162283519579483,0.08163767158765754,0.634126984126984,0.08009190991455786,0.36320050940978454,0.06018646749200912,0.5239036063335376,0.14740021344728976,0.37610884530892047,0.0760087699533074,0.6563492063492063,0.06431020501550125,0.6032694055441358,0.07290814030935075,0.6464310864983134,0.09580096959112427,0.6100615120789444,0.08078656411296503,0.9511890595408049,0.016071213375949578,0.7617961570593149,0.0689235340767993,0.958294443004062,0.05613748087593979,0.8151453855878632,0.03136647604932653,0.3412071963471256,0.07500564394419486,0.08684355805526371,0.020779098028044035 diff --git a/requirements.txt b/requirements.txt index c73d401..9bce2ae 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,22 +1,25 @@ -# 严格遵循 pixi.toml 的依赖版本 -# 注意: lnp_ml 本地包在 Dockerfile 中单独安装 - -# conda dependencies (in pixi.toml [dependencies]) -loguru -tqdm -typer - -# pypi dependencies (in pixi.toml [pypi-dependencies]) -chemprop==1.7.0 -setuptools -pandas>=2.0.3,<3 -openpyxl>=3.1.5,<4 -python-dotenv>=1.0.1,<2 -pyarrow>=17.0.0,<18 -fastparquet>=2024.2.0,<2025 -fastapi>=0.124.4,<0.125 -streamlit>=1.40.1,<2 -httpx>=0.28.1,<0.29 -uvicorn>=0.33.0,<0.34 -optuna>=4.5.0,<5 -captum>=0.7.0 +# 严格遵循 pixi.toml 的依赖版本 +# 注意: lnp_ml 本地包在 Dockerfile 中单独安装 + +# conda dependencies (in pixi.toml [dependencies]) +loguru +tqdm +typer + +# pypi dependencies (in pixi.toml [pypi-dependencies]) +chemprop==1.7.0 +setuptools +pandas>=2.0.3,<3 +openpyxl>=3.1.5,<4 +python-dotenv>=1.0.1,<2 +pyarrow>=17.0.0,<18 +fastparquet>=2024.2.0,<2025 +fastapi>=0.124.4,<0.125 +streamlit>=1.40.1,<2 +httpx>=0.28.1,<0.29 +uvicorn>=0.33.0,<0.34 +optuna>=4.5.0,<5 +captum>=0.7.0 +transformers>=4.30,<4.46 +sentencepiece +protobuf diff --git a/scripts/summarize_ablation.py b/scripts/summarize_ablation.py new file mode 100644 index 0000000..811efbd --- /dev/null +++ b/scripts/summarize_ablation.py @@ -0,0 +1,66 @@ +"""汇总消融实验结果：对比 baseline / +moe / +llm / +moe+llm 的 nested CV 指标。 + +读取每个 run 目录下的 summary.json（nested_cv 产出），输出对比表 CSV 并打印。 + +用法： + python scripts/summarize_ablation.py \ + --run baseline=models/nested_cv/baseline/ \ + --run +moe=models/nested_cv/moe/ \ + --run +llm=models/nested_cv/llm/ \ + --run +moe+llm=models/nested_cv/both/ \ + --out reports/ablation_summary.csv +""" + +from __future__ import annotations + +import argparse +import json +from pathlib import Path + +import pandas as pd + + +def load_summary_stats(run_dir: Path) -> dict: + """读取 run 目录的 summary.json，返回 summary_stats。""" + summary_path = run_dir / "summary.json" + if not summary_path.exists(): + raise FileNotFoundError(f"summary.json not found in {run_dir}") + with open(summary_path) as f: + return json.load(f).get("summary_stats", {}) + + +def main() -> None: + parser = argparse.ArgumentParser(description="Summarize ablation nested-CV runs") + parser.add_argument( + "--run", action="append", default=[], + help="LABEL=run_dir，可重复传入多个变体", + ) + parser.add_argument("--out", type=str, default="reports/ablation_summary.csv") + args = parser.parse_args() + + if not args.run: + parser.error("至少需要一个 --run LABEL=run_dir") + + rows = [] + for spec in args.run: + label, sep, run_dir = spec.partition("=") + if not sep: + parser.error(f"--run 需为 LABEL=run_dir 形式，收到: {spec}") + stats = load_summary_stats(Path(run_dir)) + row = {"variant": label, "run_dir": run_dir} + for task, metrics in stats.items(): + for metric_name, value in metrics.items(): + row[f"{task}.{metric_name}"] = value + rows.append(row) + + df = pd.DataFrame(rows) + out = Path(args.out) + out.parent.mkdir(parents=True, exist_ok=True) + df.to_csv(out, index=False) + + print(df.to_string(index=False)) + print(f"\nSaved ablation summary to {out}") + + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/scripts_run/run_all.sh b/scripts_run/run_all.sh new file mode 100644 index 0000000..eebbd56 --- /dev/null +++ b/scripts_run/run_all.sh @@ -0,0 +1,91 @@ +#!/usr/bin/env bash +set -uo pipefail +cd "$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" +export TRANSFORMERS_OFFLINE=1 HF_HUB_OFFLINE=1 MOLT5_PATH=models/molt5-base PYTHONUNBUFFERED=1 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True +mkdir -p logs models/abl models/pretrain reports + +PT=models/pretrain_delivery.pt +NT=${NT:-15}; EPT=${EPT:-30} +MAX_RETRY=${MAX_RETRY:-5} +NEED_MIB=${NEED_MIB:-8000} + +GPUS=${GPUS:-0} +IFS=',' read -r -a GPU_ARR <<< "$GPUS" +PRETRAIN_GPU=${PRETRAIN_GPU:-${GPU_ARR[0]}} + +ts(){ awk '{ print strftime("%F %T"), $0; fflush() }'; } + +retry(){ + local log=$1; shift; local n=1 start + while :; do + start=$(date +%s) + echo "[$(date '+%F %T')] >>> START (try $n/$((MAX_RETRY+1))) : $*" | tee -a "$log" + if "$@" 2>&1 | ts | tee -a "$log"; then + echo "[$(date '+%F %T')] <<< DONE ($(( ($(date +%s)-start)/60 )) min)" | tee -a "$log"; return 0; fi + (( n > MAX_RETRY )) && { echo "[$(date '+%F %T')] !!! FAILED after $n tries" | tee -a "$log"; return 1; } + echo "[$(date '+%F %T')] retry in 30s..." | tee -a "$log"; sleep 30; ((n++)) + done +} + +NR=${NR:-3} +COMMON="--device cuda --n-trials $NT --epochs-per-trial $EPT --n-repeats $NR" +wait_free(){ + while :; do + local free + free=$(nvidia-smi --query-gpu=memory.free --format=csv,noheader,nounits \ + | sed -n "$((${CUDA_VISIBLE_DEVICES:-0}+1))p") + [ "${free:-0}" -ge "$NEED_MIB" ] && return 0 + echo "[$(date '+%F %T')] GPU${CUDA_VISIBLE_DEVICES:-0} 仅剩 ${free}MiB(<${NEED_MIB}),等待空闲...(60s)" >&2 + sleep 60 + done +} + +pretrain_arch(){ local name=$1; shift + local pt="models/pretrain/${name}/pretrain_delivery.pt" + if [ ! -f "$pt" ]; then + pt_py(){ wait_free; python -m lnp_ml.modeling.pretrain main --device cuda --output-dir "models/pretrain/${name}" "$@"; } + retry "logs/pretrain_${name}.log" pt_py "$@" >&2 + fi + echo "$pt" +} + +run_variant(){ local l=$1; shift + if ls models/abl/"$l"/*/summary.json >/dev/null 2>&1; then + echo "[$(date '+%F %T')] SKIP $l (已有完成结果)"; return 0 + fi + local pt; pt=$(pretrain_arch "$l" "$@") + run_py(){ wait_free; python -m lnp_ml.modeling.nested_cv_optuna $COMMON --init-from-pretrain "$pt" "$@" --output-dir "models/abl/$l"; } + retry "logs/nested_${l}.log" run_py "$@" +} +echo "[$(date '+%F %T')] GPUS=$GPUS PRETRAIN_GPU=$PRETRAIN_GPU" + +# 0) 预训练已并入 run_variant + +# 1) 消融 +if [ "${#GPU_ARR[@]}" -ge 2 ]; then + ( export CUDA_VISIBLE_DEVICES=${GPU_ARR[0]} + run_variant baseline + run_variant both --use-moe --use-llm --llm-model-path models/molt5-base + ) & G0=$! + ( export CUDA_VISIBLE_DEVICES=${GPU_ARR[1]} + run_variant moe --use-moe + run_variant llm --use-llm --llm-model-path models/molt5-base + ) & G1=$! + wait $G0 $G1 +else + export CUDA_VISIBLE_DEVICES=${GPU_ARR[0]} + run_variant baseline + run_variant moe --use-moe + run_variant llm --use-llm --llm-model-path models/molt5-base + run_variant both --use-moe --use-llm --llm-model-path models/molt5-base +fi + +# 2) 汇总 +latest(){ ls -dt "models/abl/$1"/*/summary.json 2>/dev/null | head -1 | xargs -r dirname; } +sum_args=() +for pair in "baseline:baseline" "+moe:moe" "+llm:llm" "+both:both"; do + label=${pair%%:*}; name=${pair##*:}; d=$(latest "$name") + [ -n "$d" ] && sum_args+=(--run "${label}=$d") +done +python scripts/summarize_ablation.py "${sum_args[@]}" \ + --out reports/ablation_summary.csv 2>&1 | ts | tee logs/summary.log \ No newline at end of file