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https://github.com/RYDE-WORK/lnp_ml.git
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Add MoleculeSTM/MolE/RF/KNN baselines: encoders, scripts, and CV results
This commit is contained in:
Michelle0574
parent
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commit
bab8203c98
8
.gitignore
vendored
8
.gitignore
vendored
@ -202,3 +202,11 @@ models/**/*.sqlite3
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models/biot5-plus-base/
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data/processed/*.npz
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MolE_ckpt/
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MoleculeSTM_ckpt/
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MolE_repo/
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MoleculeSTM_repo/
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data/processed/mole_representation.tsv.gz
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data/processed/mole_input_smiles.csv
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@ -2,10 +2,14 @@ from lnp_ml.modeling.encoders.rdkit_encoder import CachedRDKitEncoder
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from lnp_ml.modeling.encoders.mpnn_encoder import CachedMPNNEncoder
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from lnp_ml.modeling.encoders.chemeleon_encoder import CheMeleonEmbeddingEncoder
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from lnp_ml.modeling.encoders.unimol_encoder import UniMolEmbeddingEncoder
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from lnp_ml.modeling.encoders.moleculestm_encoder import MoleculeSTMEmbeddingEncoder
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from lnp_ml.modeling.encoders.mole_encoder import MolEEmbeddingEncoder
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__all__ = [
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"CachedRDKitEncoder",
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"CachedMPNNEncoder",
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"CheMeleonEmbeddingEncoder",
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"UniMolEmbeddingEncoder",
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"MoleculeSTMEmbeddingEncoder",
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"MolEEmbeddingEncoder",
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]
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48
lnp_ml/modeling/encoders/mole_encoder.py
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48
lnp_ml/modeling/encoders/mole_encoder.py
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@ -0,0 +1,48 @@
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"""MolE 预计算表征的查表编码器"""
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from pathlib import Path
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from typing import Dict, List
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import numpy as np
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import torch
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import torch.nn as nn
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class MolEEmbeddingEncoder(nn.Module):
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"""从预计算 .npz 缓存按 SMILES 查 MolE 表征，返回 {"mole": [B, D]}。"""
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def __init__(self, cache_path: str) -> None:
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super().__init__()
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self.cache_path = str(cache_path)
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self._table: Dict[str, np.ndarray] = {}
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self._embed_dim: int = 0
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self._load()
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def _load(self) -> None:
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path = Path(self.cache_path)
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if not path.exists():
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raise FileNotFoundError(
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f"MolE 缓存不存在: {path}。请先运行 scripts/precompute_mole.py。"
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)
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data = np.load(path, allow_pickle=True)
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embeddings = np.asarray(data["embeddings"], dtype=np.float32)
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if embeddings.ndim != 2:
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raise ValueError(f"embeddings 应为 2D，实际 {embeddings.shape}")
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self._embed_dim = int(embeddings.shape[1])
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self._table = {str(s): embeddings[i] for i, s in enumerate(data["smiles"])}
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def forward(self, smiles_list: List[str]) -> Dict[str, torch.Tensor]:
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missing = [s for s in smiles_list if s not in self._table]
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if missing:
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raise KeyError(
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f"{len(missing)} 个 SMILES 不在 MolE 缓存中，请重跑 precompute_mole.py。"
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f"示例: {missing[:3]}"
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)
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mat = np.stack([self._table[s] for s in smiles_list])
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return {"mole": torch.from_numpy(mat)}
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def clear_cache(self) -> None:
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"""查表器无临时缓存，仅为接口对齐。"""
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@property
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def embed_dim(self) -> int:
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return self._embed_dim
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48
lnp_ml/modeling/encoders/moleculestm_encoder.py
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48
lnp_ml/modeling/encoders/moleculestm_encoder.py
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@ -0,0 +1,48 @@
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"""MoleculeSTM 预计算表征的查表编码器"""
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from pathlib import Path
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from typing import Dict, List
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import numpy as np
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import torch
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import torch.nn as nn
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class MoleculeSTMEmbeddingEncoder(nn.Module):
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"""从预计算 .npz 缓存按 SMILES 查 MoleculeSTM 表征，返回 {"moleculestm": [B, D]}。"""
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def __init__(self, cache_path: str) -> None:
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super().__init__()
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self.cache_path = str(cache_path)
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self._table: Dict[str, np.ndarray] = {}
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self._embed_dim: int = 0
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self._load()
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def _load(self) -> None:
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path = Path(self.cache_path)
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if not path.exists():
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raise FileNotFoundError(
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f"MoleculeSTM 缓存不存在: {path}。请先运行 scripts/precompute_moleculestm.py。"
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)
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data = np.load(path, allow_pickle=True)
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embeddings = np.asarray(data["embeddings"], dtype=np.float32)
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if embeddings.ndim != 2:
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raise ValueError(f"embeddings 应为 2D，实际 {embeddings.shape}")
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self._embed_dim = int(embeddings.shape[1])
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self._table = {str(s): embeddings[i] for i, s in enumerate(data["smiles"])}
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def forward(self, smiles_list: List[str]) -> Dict[str, torch.Tensor]:
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missing = [s for s in smiles_list if s not in self._table]
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if missing:
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raise KeyError(
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f"{len(missing)} 个 SMILES 不在 MoleculeSTM 缓存中，请重跑 precompute_moleculestm.py。"
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f"示例: {missing[:3]}"
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)
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mat = np.stack([self._table[s] for s in smiles_list])
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return {"moleculestm": torch.from_numpy(mat)}
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def clear_cache(self) -> None:
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"""查表器无临时缓存，仅为接口对齐。"""
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@property
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def embed_dim(self) -> int:
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return self._embed_dim
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@ -9,6 +9,8 @@ from lnp_ml.modeling.encoders import (
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CachedMPNNEncoder,
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CheMeleonEmbeddingEncoder,
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UniMolEmbeddingEncoder,
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MoleculeSTMEmbeddingEncoder,
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MolEEmbeddingEncoder,
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)
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from lnp_ml.modeling.layers import (
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TokenProjector,
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@ -100,6 +102,10 @@ class LNPModel(nn.Module):
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chemeleon_cache_path: Optional[str] = None,
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# UniMol encoder
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unimol_cache_path: Optional[str] = None,
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# MoleculeSTM encoder
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moleculestm_cache_path: Optional[str] = None,
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# MolE encoder
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mole_cache_path: Optional[str] = None,
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# 输入维度配置
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input_dims: Optional[Dict[str, int]] = None,
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# ============ MoE 相关 ============
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@ -132,6 +138,8 @@ class LNPModel(nn.Module):
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self.use_mpnn = mpnn_checkpoint is not None or mpnn_ensemble_paths is not None
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self.use_chemeleon = chemeleon_cache_path is not None
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self.use_unimol = unimol_cache_path is not None
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self.use_moleculestm = moleculestm_cache_path is not None
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self.use_mole = mole_cache_path is not None
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# ============ Encoders ============
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self.rdkit_encoder = CachedRDKitEncoder()
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@ -156,6 +164,18 @@ class LNPModel(nn.Module):
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else:
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self.unimol_encoder = None
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if self.use_moleculestm:
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self.moleculestm_encoder = MoleculeSTMEmbeddingEncoder(cache_path=moleculestm_cache_path)
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self.input_dims = {**self.input_dims, "moleculestm": self.moleculestm_encoder.embed_dim}
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else:
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self.moleculestm_encoder = None
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if self.use_mole:
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self.mole_encoder = MolEEmbeddingEncoder(cache_path=mole_cache_path)
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self.input_dims = {**self.input_dims, "mole": self.mole_encoder.embed_dim}
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else:
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self.mole_encoder = None
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# ============ Token Projector ============
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proj_input_dims = {k: v for k, v in self.input_dims.items()}
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if not self.use_mpnn:
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@ -174,6 +194,10 @@ class LNPModel(nn.Module):
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chem_keys.append("chemeleon")
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if self.use_unimol:
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chem_keys.append("unimol")
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if self.use_moleculestm:
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chem_keys.append("moleculestm")
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if self.use_mole:
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chem_keys.append("mole")
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chem_keys += ["morgan", "maccs", "desc"]
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self.chem_keys = chem_keys
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self.tab_keys = TAB_KEYS
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@ -268,6 +292,10 @@ class LNPModel(nn.Module):
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all_features["chemeleon"] = self.chemeleon_encoder(smiles)["chemeleon"].to(device)
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if self.use_unimol:
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all_features["unimol"] = self.unimol_encoder(smiles)["unimol"].to(device)
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if self.use_moleculestm:
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all_features["moleculestm"] = self.moleculestm_encoder(smiles)["moleculestm"].to(device)
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if self.use_mole:
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all_features["mole"] = self.mole_encoder(smiles)["mole"].to(device)
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all_features["morgan"] = rdkit_features["morgan"].to(device)
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all_features["maccs"] = rdkit_features["maccs"].to(device)
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all_features["desc"] = rdkit_features["desc"].to(device)
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@ -493,6 +521,8 @@ class LNPModelWithoutMPNN(LNPModel):
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input_dims: Optional[Dict[str, int]] = None,
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chemeleon_cache_path: Optional[str] = None,
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unimol_cache_path: Optional[str] = None,
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moleculestm_cache_path: Optional[str] = None,
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mole_cache_path: Optional[str] = None,
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# ============ MoE 相关 ============
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use_moe: bool = False,
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moe_n_experts: int = 4,
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@ -531,6 +561,8 @@ class LNPModelWithoutMPNN(LNPModel):
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mpnn_ensemble_paths=None,
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chemeleon_cache_path=chemeleon_cache_path,
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unimol_cache_path=unimol_cache_path,
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moleculestm_cache_path=moleculestm_cache_path,
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mole_cache_path=mole_cache_path,
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input_dims=dims,
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reg_bypass=reg_bypass,
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use_moe=use_moe,
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@ -197,6 +197,8 @@ def create_model(
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mpnn_device: str = "cpu",
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chemeleon_cache: Optional[str] = None,
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unimol_cache: Optional[str] = None,
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moleculestm_cache: Optional[str] = None,
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mole_cache: Optional[str] = None,
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set_transformer_block: str = "sab",
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# MoE
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use_moe: bool = False,
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@ -222,6 +224,8 @@ def create_model(
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retr_feature_dim=retr_feature_dim,
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chemeleon_cache_path=chemeleon_cache,
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unimol_cache_path=unimol_cache,
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moleculestm_cache_path=moleculestm_cache,
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mole_cache_path=mole_cache,
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**(llm_kwargs or {}),
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)
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@ -424,6 +428,8 @@ def run_inner_optuna(
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use_mpnn: bool = False,
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chemeleon_cache: Optional[str] = None,
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unimol_cache: Optional[str] = None,
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moleculestm_cache: Optional[str] = None,
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mole_cache: Optional[str] = None,
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seed: int = 42,
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study_path: Optional[Path] = None,
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pretrain_state_dict: Optional[Dict] = None,
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@ -544,6 +550,8 @@ def run_inner_optuna(
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mpnn_device=device.type,
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chemeleon_cache=chemeleon_cache,
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unimol_cache=unimol_cache,
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moleculestm_cache=moleculestm_cache,
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mole_cache=mole_cache,
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use_moe=use_moe,
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moe_n_experts=moe_ne_t,
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moe_top_k=moe_tk_t,
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@ -641,6 +649,8 @@ def _run_single_outer_fold(
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use_mpnn: bool,
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chemeleon_cache: Optional[str],
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unimol_cache: Optional[str],
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moleculestm_cache: Optional[str],
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mole_cache: Optional[str],
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seed: int,
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pretrain_state_dict: Optional[Dict],
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pretrain_config: Optional[Dict],
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@ -744,6 +754,8 @@ def _run_single_outer_fold(
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use_mpnn=use_mpnn,
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chemeleon_cache=chemeleon_cache,
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unimol_cache=unimol_cache,
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moleculestm_cache=moleculestm_cache,
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mole_cache=mole_cache,
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seed=seed + outer_fold,
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study_path=study_path,
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pretrain_state_dict=pretrain_state_dict,
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@ -803,6 +815,8 @@ def _run_single_outer_fold(
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mpnn_device=device.type,
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chemeleon_cache=chemeleon_cache,
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unimol_cache=unimol_cache,
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moleculestm_cache=moleculestm_cache,
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mole_cache=mole_cache,
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use_moe=use_moe,
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moe_n_experts=best_params.get("moe_n_experts", moe_n_experts),
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moe_top_k=best_params.get("moe_top_k", moe_top_k),
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@ -871,6 +885,10 @@ def _run_single_outer_fold(
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"chemeleon_cache": chemeleon_cache,
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"use_unimol": unimol_cache is not None,
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"unimol_cache": unimol_cache,
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"use_moleculestm": moleculestm_cache is not None,
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"moleculestm_cache": moleculestm_cache,
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"use_mole": mole_cache is not None,
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"mole_cache": mole_cache,
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"use_moe": use_moe,
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"moe_n_experts": moe_n_experts,
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"moe_top_k": moe_top_k,
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@ -943,6 +961,10 @@ def main(
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chemeleon_cache: str = "data/processed/chemeleon_embeddings.npz",
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use_unimol: bool = False,
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unimol_cache: str = "data/processed/unimol_embeddings.npz",
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use_moleculestm: bool = False,
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moleculestm_cache: str = "data/processed/moleculestm_embeddings.npz",
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use_mole: bool = False,
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mole_cache: str = "data/processed/mole_embeddings.npz",
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n_repeats: int = 1,
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repeat_seed_step: int = 1000,
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# MoE（消融开关）
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@ -1075,6 +1097,8 @@ def main(
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use_mpnn=use_mpnn,
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chemeleon_cache=(chemeleon_cache if use_chemeleon else None),
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unimol_cache=(unimol_cache if use_unimol else None),
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moleculestm_cache=(moleculestm_cache if use_moleculestm else None),
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mole_cache=(mole_cache if use_mole else None),
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seed=seed,
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pretrain_state_dict=pretrain_state_dict,
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pretrain_config=pretrain_config,
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42
models/abl_full/rf/seed42/outer_fold_0/test_metrics.json
Normal file
42
models/abl_full/rf/seed42/outer_fold_0/test_metrics.json
Normal file
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{
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"size": {
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"n_samples": 83,
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"mse": 1.368858521000182,
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"rmse": 1.1699822737974204,
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"mae": 0.48265016079386586,
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"r2": 0.16222489685168928
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},
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"delivery": {
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"n_samples": 58,
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"mse": 0.4106486248647463,
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"rmse": 0.6408187145088275,
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"mae": 0.4092221012826776,
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"r2": 0.4780408138229023
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},
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"toxic": {
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"n_samples": 58,
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"accuracy": 1.0,
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"precision": 1.0,
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"recall": 1.0,
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"f1": 1.0
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},
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"pdi": {
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"n_samples": 84,
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"accuracy": 0.8095238095238095,
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"precision": 0.7797101449275362,
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"recall": 0.7063435495367071,
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"f1": 0.7279352226720648
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},
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"ee": {
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"n_samples": 84,
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"accuracy": 0.75,
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"precision": 0.6912280701754385,
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"recall": 0.6471428571428571,
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"f1": 0.6631611379274931
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},
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"biodist": {
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"n_samples": 58,
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"kl_divergence": 0.16366098804686893,
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"js_divergence": 0.0359968761663274
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}
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}
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42
models/abl_full/rf/seed42/outer_fold_1/test_metrics.json
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42
models/abl_full/rf/seed42/outer_fold_1/test_metrics.json
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@ -0,0 +1,42 @@
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{
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"size": {
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"n_samples": 84,
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"mse": 0.30493822479834903,
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"rmse": 0.5522121193874225,
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"mae": 0.34891908283267253,
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"r2": 0.0688270762246912
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},
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"delivery": {
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"n_samples": 61,
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"mse": 0.7776284206466513,
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"rmse": 0.8818324220886026,
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"mae": 0.519796750021438,
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"r2": 0.4113913613868899
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},
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"toxic": {
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"n_samples": 61,
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"accuracy": 1.0,
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"precision": 1.0,
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"recall": 1.0,
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"f1": 1.0
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},
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"pdi": {
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"n_samples": 84,
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"accuracy": 0.7619047619047619,
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"precision": 0.6711111111111111,
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"recall": 0.5873015873015873,
|
||||
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42
models/abl_full/rf/seed42/outer_fold_2/test_metrics.json
Normal file
42
models/abl_full/rf/seed42/outer_fold_2/test_metrics.json
Normal file
@ -0,0 +1,42 @@
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42
models/abl_full/rf/seed42/outer_fold_3/test_metrics.json
Normal file
42
models/abl_full/rf/seed42/outer_fold_3/test_metrics.json
Normal file
@ -0,0 +1,42 @@
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||||
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42
models/abl_full/rf/seed42/outer_fold_4/test_metrics.json
Normal file
42
models/abl_full/rf/seed42/outer_fold_4/test_metrics.json
Normal file
@ -0,0 +1,42 @@
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287
models/abl_full/rf/seed42/summary.json
Normal file
287
models/abl_full/rf/seed42/summary.json
Normal file
@ -0,0 +1,287 @@
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@ -0,0 +1,16 @@
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372
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12
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Normal file
12
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Normal file
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300
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300
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Normal file
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1
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1
models/abl_full/s2_mole/seed42/outer_fold_0/splits.json
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12
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Normal file
12
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153
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Normal file
153
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Normal file
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1
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1
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12
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Normal file
12
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216
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1
models/abl_full/s2_mole/seed42/outer_fold_2/splits.json
Normal file
1
models/abl_full/s2_mole/seed42/outer_fold_2/splits.json
Normal file
@ -0,0 +1 @@
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12
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Normal file
12
models/abl_full/s2_mole/seed42/outer_fold_3/best_params.json
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237
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Normal file
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1
models/abl_full/s2_mole/seed42/outer_fold_3/splits.json
Normal file
1
models/abl_full/s2_mole/seed42/outer_fold_3/splits.json
Normal file
@ -0,0 +1 @@
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12
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12
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1
models/abl_full/s2_mole/seed42/outer_fold_4/splits.json
Normal file
1
models/abl_full/s2_mole/seed42/outer_fold_4/splits.json
Normal file
@ -0,0 +1 @@
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@ -0,0 +1,42 @@
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60
models/abl_full/s2_mole/seed42/strata_info.json
Normal file
60
models/abl_full/s2_mole/seed42/strata_info.json
Normal file
@ -0,0 +1,60 @@
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
352
models/abl_full/s2_mole/seed42/summary.json
Normal file
352
models/abl_full/s2_mole/seed42/summary.json
Normal file
@ -0,0 +1,352 @@
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||||
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@ -0,0 +1 @@
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@ -0,0 +1 @@
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60
models/abl_full/s2_moleculestm/seed42/strata_info.json
Normal file
60
models/abl_full/s2_moleculestm/seed42/strata_info.json
Normal file
@ -0,0 +1,60 @@
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352
models/abl_full/s2_moleculestm/seed42/summary.json
Normal file
352
models/abl_full/s2_moleculestm/seed42/summary.json
Normal file
@ -0,0 +1,352 @@
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287
models/abl_full/tanimoto_knn/seed42/summary.json
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287
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
@ -0,0 +1,42 @@
|
||||
{
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||||
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||||
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||||
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||||
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||||
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||||
}
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
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Reference in New Issue
Block a user