From 22e83122e73442a6609e1b4a269497fe046a0503 Mon Sep 17 00:00:00 2001 From: Michelle0574 <2170308303@qq.com> Date: Tue, 7 Jul 2026 17:47:34 +0000 Subject: [PATCH] =?UTF-8?q?feat:=20Qwen=20SoftRAG=EF=BC=88RAG=20prompt/sof?= =?UTF-8?q?t=20token/QLoRA=EF=BC=89+=20Morgan=20=E6=A3=80=E7=B4=A2?= =?UTF-8?q?=E6=97=81=E8=B7=AF=E4=B8=8E=E5=AE=9E=E9=AA=8C=E7=BB=93=E6=9E=9C?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .gitignore | 5 +- lnp_ml/modeling/layers/fusion.py | 7 +- lnp_ml/modeling/layers/llm_prompt.py | 267 +++-- lnp_ml/modeling/models.py | 38 +- lnp_ml/modeling/nested_cv_optuna.py | 82 +- lnp_ml/modeling/retrieval.py | 85 ++ lnp_ml/modeling/trainer_balanced.py | 6 +- .../outer_fold_0/best_params.json | 12 - .../outer_fold_0/test_metrics.json | 42 - .../outer_fold_1/best_params.json | 12 - .../outer_fold_1/test_metrics.json | 42 - .../outer_fold_2/best_params.json | 12 - .../outer_fold_2/test_metrics.json | 42 - 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models/{abl/baseline/20260617_180556 => abl_qwen_softrag/seed42}/strata_info.json (100%) create mode 100644 models/abl_qwen_softrag/seed42/summary.json create mode 100644 scripts_run/run_qwen_softrag.sh diff --git a/.gitignore b/.gitignore index 20744af..7cba4c5 100644 --- a/.gitignore +++ b/.gitignore @@ -195,4 +195,7 @@ logs/ models/**/*.pt models/**/*.png models/molt5-base/ -models/abl/ \ No newline at end of file +models/abl/ +models/qwen2.5-7b-instruct/ +models/**/*.sqlite3tests/ +models/**/*.sqlite3 diff --git a/lnp_ml/modeling/layers/fusion.py b/lnp_ml/modeling/layers/fusion.py index 70ef066..99e1fd8 100644 --- a/lnp_ml/modeling/layers/fusion.py +++ b/lnp_ml/modeling/layers/fusion.py @@ -128,6 +128,7 @@ class ResidualConcatFusion(nn.Module): # 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(())) + self.g_retr = nn.Parameter(torch.zeros(())) def forward( self, @@ -135,9 +136,9 @@ class ResidualConcatFusion(nn.Module): tab: torch.Tensor, f_moe: Optional[torch.Tensor] = None, f_llm: Optional[torch.Tensor] = None, + f_retr: 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: @@ -145,8 +146,10 @@ class ResidualConcatFusion(nn.Module): out = pooled if f_moe is not None: - out = out + self.g_moe * f_moe # 残差 + 零初始化门 + 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 + if f_retr is not None: + out = out + self.g_retr * f_retr 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 index 824dfef..e15c3d5 100644 --- a/lnp_ml/modeling/layers/llm_prompt.py +++ b/lnp_ml/modeling/layers/llm_prompt.py @@ -1,4 +1,4 @@ -"""LLM 分子特征分支：用 MolT5 直接编码 SMILES 文本。""" +"""LLM 分子特征分支：SMILES 文本 + chem'/tab soft token 的混合 prompt 编码。""" import os from typing import Dict, List, Optional @@ -7,15 +7,20 @@ 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") +# 检索池支持的额外多任务（除 delivery 外） +_EXTRA_TASKS = ["size", "pdi", "ee", "toxic", "biodist"] + class LLMPromptEncoder(nn.Module): - """用 MolT5 编码 SMILES 文本，输出分子特征 F_llm [B, d_model]。 + """用 LLM 编码分子，输出 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。 + 三种模式： + - use_soft_prompt=True：原始 SMILES 文本(+RAG 邻居) + chem'/tab soft token， + inputs_embeds 注入，全程带梯度（配合 LoRA/QLoRA 真微调），不缓存特征。 + - use_rag=True 且非 soft：旧的冻结+缓存 RAG 路径。 + - 其他：冻结缓存 / 可训练 mean-pool。 """ def __init__( @@ -26,33 +31,50 @@ class LLMPromptEncoder(nn.Module): model_name_or_path: str = DEFAULT_MOLT5_PATH, freeze: bool = True, use_lora: bool = False, + use_qlora: bool = False, lora_r: int = 8, lora_alpha: int = 16, lora_dropout: float = 0.05, - max_length: int = 128, + max_length: int = 256, use_rag: bool = False, rag_top_k: int = 4, + use_soft_prompt: bool = False, ) -> None: super().__init__() from transformers import AutoTokenizer, T5EncoderModel, AutoModel - self.use_lora = use_lora + self.use_lora = use_lora or use_qlora + self.use_qlora = use_qlora self.max_length = max_length + self.use_rag = use_rag + self.rag_top_k = rag_top_k + self.use_soft_prompt = use_soft_prompt + _name_l = model_name_or_path.lower() _is_t5 = "t5" in _name_l _is_qwen = "qwen" in _name_l - self.use_rag = use_rag - self.rag_top_k = rag_top_k self._is_qwen = _is_qwen - # Qwen 等大模型 tokenizer 需要 trust_remote_code + self.tokenizer = AutoTokenizer.from_pretrained( model_name_or_path, trust_remote_code=_is_qwen) if _is_qwen and self.tokenizer.pad_token is None: self.tokenizer.pad_token = self.tokenizer.eos_token - # 自动判断架构：T5→T5EncoderModel；Qwen(decoder)→AutoModel(fp16)；其他→AutoModel + if _is_t5: self.encoder = T5EncoderModel.from_pretrained(model_name_or_path) self.hidden_size = self.encoder.config.d_model + elif _is_qwen and use_qlora: + from transformers import BitsAndBytesConfig + bnb = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_use_double_quant=True, + bnb_4bit_compute_dtype=torch.bfloat16, + ) + self.encoder = AutoModel.from_pretrained( + model_name_or_path, trust_remote_code=True, + quantization_config=bnb, torch_dtype=torch.bfloat16) + self.hidden_size = self.encoder.config.hidden_size elif _is_qwen: self.encoder = AutoModel.from_pretrained( model_name_or_path, trust_remote_code=True, torch_dtype=torch.float16) @@ -61,31 +83,45 @@ class LLMPromptEncoder(nn.Module): self.encoder = AutoModel.from_pretrained(model_name_or_path) self.hidden_size = self.encoder.config.hidden_size - if use_lora: - self._apply_lora(lora_r, lora_alpha, lora_dropout) + if self.use_lora: + self._apply_lora(lora_r, lora_alpha, lora_dropout, prepare_kbit=use_qlora) elif freeze: for p in self.encoder.parameters(): p.requires_grad = False - self._frozen = freeze and not use_lora + self._frozen = freeze and not self.use_lora + + # soft token 投影：chem'(d_model) / tab(d_model) -> hidden_size + if use_soft_prompt: + self.chem_to_llm = nn.Linear(d_model, self.hidden_size) + self.tab_to_llm = nn.Linear(d_model, self.hidden_size) + else: + self.chem_to_llm = None + self.tab_to_llm = None 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） + + # 缓存：冻结句向量缓存 / 文本 prompt 缓存 self._cache: Dict[str, torch.Tensor] = {} - # RAG 检索池（防泄漏：由 nested_cv 在每个 outer fold 用训练集设置） + self._prompt_cache: Dict[str, str] = {} + + # RAG 检索池 self._rag_pool_smiles: List[str] = [] - self._rag_pool_labels = None # np.ndarray [N] - self._rag_pool_fps = None # 检索池指纹 - self._rag_pool_id: str = "none" # 池子标识，用于缓存隔离 + self._rag_pool_labels = None # delivery [N] + self._rag_pool_extra = None # dict: task -> (values, valid) + self._rag_pool_fps = None + self._rag_pool_id: str = "none" - def _apply_lora(self, r: int, alpha: int, dropout: float) -> None: - from peft import LoraConfig, get_peft_model + def _apply_lora(self, r, alpha, dropout, prepare_kbit=False): + from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training + + if prepare_kbit: + self.encoder = prepare_model_for_kbit_training(self.encoder) + else: + for p in self.encoder.parameters(): + p.requires_grad = False - for p in self.encoder.parameters(): - p.requires_grad = False - # 不同架构注意力层命名不同： - # T5: q/k/v/o；Roberta/ChemBERTa: query/key/value；Qwen/Llama: q_proj/k_proj/v_proj/o_proj _enc_name = type(self.encoder).__name__.lower() if getattr(self, "_is_qwen", False) or "qwen" in _enc_name or "llama" in _enc_name: _targets = ["q_proj", "k_proj", "v_proj", "o_proj"] @@ -93,27 +129,27 @@ class LLMPromptEncoder(nn.Module): _targets = ["q", "k", "v", "o"] else: _targets = ["query", "key", "value"] - cfg = LoraConfig( - r=r, lora_alpha=alpha, lora_dropout=dropout, - target_modules=_targets, bias="none", - ) + cfg = LoraConfig(r=r, lora_alpha=alpha, lora_dropout=dropout, + target_modules=_targets, bias="none") self.encoder = get_peft_model(self.encoder, cfg) - def set_retrieval_pool(self, smiles_list, labels, pool_id: str = "train") -> None: - """设置 RAG 检索池（防泄漏关键：只传训练集的 smiles 和 delivery 标签）。 - labels: array-like [N] delivery 标签。pool_id: 池标识，切换时清 RAG 缓存。""" + # ---------- 检索池 ---------- + def set_retrieval_pool(self, smiles_list, labels, pool_id="train", extra_labels=None): + """设置 RAG 检索池（防泄漏：只传训练集分子与标签）。 + labels: delivery [N]; extra_labels: dict task -> (values, valid_bool)。""" import numpy as np from lnp_ml.modeling.retrieval import _smiles_to_fp self._rag_pool_smiles = list(smiles_list) self._rag_pool_labels = np.asarray(labels, dtype=np.float32).reshape(-1) + self._rag_pool_extra = extra_labels self._rag_pool_fps = [_smiles_to_fp(s) for s in self._rag_pool_smiles] if pool_id != self._rag_pool_id: - # 池子变了，清掉 RAG 编码缓存（避免跨 fold 串用） self._cache = {k: v for k, v in self._cache.items() if not k.startswith("RAG::")} + self._prompt_cache.clear() self._rag_pool_id = pool_id def _retrieve_topk(self, query_smiles: str): - """检索 Top-K 相似邻居，返回 [(smiles, sim, label)]。排除查询分子自己（防泄漏）。""" + """检索 Top-K 邻居，返回 [dict(smiles, sim, delivery, extra)]；排除查询分子自己。""" from rdkit import DataStructs from lnp_ml.modeling.retrieval import _smiles_to_fp qfp = _smiles_to_fp(query_smiles) @@ -121,105 +157,148 @@ class LLMPromptEncoder(nn.Module): return [] sims = [] for j, (smi, fp) in enumerate(zip(self._rag_pool_smiles, self._rag_pool_fps)): - if fp is None or smi == query_smiles: # 排除自己 + if fp is None or smi == query_smiles: continue sims.append((j, DataStructs.TanimotoSimilarity(qfp, fp))) sims.sort(key=lambda t: t[1], reverse=True) out = [] - for j, sim in sims[:self.rag_top_k]: - out.append((self._rag_pool_smiles[j], sim, float(self._rag_pool_labels[j]))) + for j, sim in sims[: self.rag_top_k]: + extra = {} + if self._rag_pool_extra is not None: + for task in _EXTRA_TASKS: + if task in self._rag_pool_extra: + vals, valid = self._rag_pool_extra[task] + extra[task] = vals[j] if bool(valid[j]) else None + else: + extra[task] = None + out.append({ + "smiles": self._rag_pool_smiles[j], + "sim": sim, + "delivery": float(self._rag_pool_labels[j]), + "extra": extra, + }) return out + @staticmethod + def _fmt(v, kind="float"): + if v is None: + return "unknown" + if kind == "int": + return str(int(v)) + if kind == "vec": + return "[" + ", ".join(f"{x:.3f}" for x in v) + "]" + return f"{float(v):.3f}" + def _build_rag_prompt(self, target_smiles: str, neighbors) -> str: - """构造 RAG prompt（与探针验证版一致，已验证 R²=0.1357）。""" + """构造 RAG prompt：原始 SMILES + 邻居多任务结果（numeric）。""" blocks = [] - for rank, (smi, sim, lbl) in enumerate(neighbors, 1): + for rank, nb in enumerate(neighbors, 1): + ex = nb["extra"] blocks.append( - f"Retrieved sample {rank}:\nSMILES: {smi}\n" - f"Similarity score: {sim:.3f}\nKnown outcome - delivery_log: {lbl:.3f}" + f"Retrieved sample {rank}:\n" + f"SMILES: {nb['smiles']}\n" + f"Similarity score: {nb['sim']:.3f}\n" + f"delivery_log: {self._fmt(nb['delivery'])}\n" + f"size_z: {self._fmt(ex.get('size'))}\n" + f"pdi_class: {self._fmt(ex.get('pdi'), 'int')}\n" + f"ee_class: {self._fmt(ex.get('ee'), 'int')}\n" + f"toxic: {self._fmt(ex.get('toxic'), 'int')}\n" + f"biodist: {self._fmt(ex.get('biodist'), 'vec')}" ) retrieved_block = "\n\n".join(blocks) if blocks else "(no retrieved samples)" return ( "Task: Encode the target LNP molecule into a retrieval-aware representation " - "for downstream delivery prediction. Do not output predictions.\n\n" + "for downstream multi-task property prediction. Do not output predictions.\n\n" f"[Target Molecule]\nSMILES: {target_smiles}\n\n" "[Retrieved Similar LNP Samples]\n" - "The following are retrieved from the training set by fingerprint similarity, " - "with their known delivery outcomes:\n\n" + "Retrieved from the training set by fingerprint similarity, with their known " + "multi-task outcomes (delivery_log, size_z, pdi_class, ee_class, toxic, biodist; " + "'unknown' means the measurement is missing):\n\n" f"{retrieved_block}\n\n" "[Encoding Instructions]\n" - "Capture the target molecular structure and the retrieval evidence " - "(structural similarity and consistency of retrieved delivery outcomes) " - "into your internal representation." + "Capture the target structure and the retrieval evidence (structural similarity " + "and consistency of retrieved outcomes) into your internal representation." ) - @torch.no_grad() - def _encode_rag(self, smiles, device): - """RAG 编码：每个分子→检索→构造prompt→Qwen编码→取最后有效token。带缓存。""" - feats = [] - to_compute = [] - keys = [] - for s in smiles: - key = f"RAG::{self._rag_pool_id}::{s}" - keys.append(key) - if key not in self._cache: - to_compute.append(s) - # 逐个构造 prompt（检索是 CPU 操作） - if to_compute: - uniq = list(dict.fromkeys(to_compute)) - prompts = [self._build_rag_prompt(s, self._retrieve_topk(s)) for s in uniq] - for i in range(0, len(uniq), 4): - bt = prompts[i:i+4] - enc = self.tokenizer( - bt, padding=True, truncation=True, - max_length=self.max_length, return_tensors="pt", - ).to(device) - out = self.encoder(**enc).last_hidden_state # [B,L,H] - lengths = enc["attention_mask"].sum(1) - 1 # 最后有效token位置 - b = torch.arange(out.size(0), device=device) - last_tok = out[b, lengths.long(), :] # [B,H] - for s, v in zip(uniq[i:i+4], last_tok): - self._cache[f"RAG::{self._rag_pool_id}::{s}"] = v.float().cpu() - return torch.stack([self._cache[k] for k in keys]).to(device) + def _get_prompt(self, s: str) -> str: + if not self.use_rag: + return s + key = f"{self._rag_pool_id}::{s}" + if key not in self._prompt_cache: + self._prompt_cache[key] = self._build_rag_prompt(s, self._retrieve_topk(s)) + return self._prompt_cache[key] - def _mean_pool(self, last_hidden: torch.Tensor, mask: torch.Tensor) -> torch.Tensor: - m = mask.unsqueeze(-1).float() # [B, L, 1] + # ---------- soft-prompt 编码（带梯度，不缓存特征）---------- + def _encode_softrag(self, smiles, chem, tab, device) -> torch.Tensor: + prompts = [self._get_prompt(s) for s in smiles] + enc = self.tokenizer( + prompts, padding=True, truncation=True, + max_length=self.max_length, return_tensors="pt", + ).to(device) + + embed_layer = self.encoder.get_input_embeddings() + text_embeds = embed_layer(enc["input_ids"]) # [B, L, H] + text_mask = enc["attention_mask"] # [B, L] + + soft_list = [] + if self.chem_to_llm is not None and chem is not None: + soft_list.append(self.chem_to_llm(chem)) # [B, n_chem, H] + if self.tab_to_llm is not None and tab is not None: + soft_list.append(self.tab_to_llm(tab)) # [B, n_tab, H] + + if soft_list: + soft = torch.cat(soft_list, dim=1).to(text_embeds.dtype) + inputs_embeds = torch.cat([soft, text_embeds], dim=1) + soft_mask = torch.ones(soft.size(0), soft.size(1), + device=device, dtype=text_mask.dtype) + attn_mask = torch.cat([soft_mask, text_mask], dim=1) + else: + inputs_embeds = text_embeds + attn_mask = text_mask + + out = self.encoder(inputs_embeds=inputs_embeds, attention_mask=attn_mask).last_hidden_state + lengths = attn_mask.sum(1) - 1 + b = torch.arange(out.size(0), device=device) + feat = out[b, lengths.long(), :] # [B, H] 最后有效 token + return self.proj_down(feat.float()) + + # ---------- 旧路径（保留，向后兼容）---------- + def _mean_pool(self, last_hidden, mask): + m = mask.unsqueeze(-1).float() 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: + def _encode_frozen(self, smiles, device): 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) + 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) + def _encode_trainable(self, smiles, device): + 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]。""" + def forward(self, smiles: List[str], chem: Optional[torch.Tensor] = None, + tab: Optional[torch.Tensor] = None) -> torch.Tensor: device = self.proj_down[0].weight.device + if self.use_soft_prompt: + return self._encode_softrag(smiles, chem, tab, device) if self.use_rag: - feat = self._encode_rag(smiles, device) - else: - feat = self._encode_frozen(smiles, device) if self._frozen \ - else self._encode_trainable(smiles, device) + return self._encode_softrag(smiles, None, None, 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 + self._cache.clear() + self._prompt_cache.clear() \ No newline at end of file diff --git a/lnp_ml/modeling/models.py b/lnp_ml/modeling/models.py index 10fcd36..edd9510 100644 --- a/lnp_ml/modeling/models.py +++ b/lnp_ml/modeling/models.py @@ -20,17 +20,26 @@ PoolingStrategy = Literal["attention", "avg", "max"] # Token 维度配置 +def _infer_desc_dim() -> int: + """RDKit 描述符数量随版本变化，运行时实测，避免写死导致 BatchNorm 维度不匹配。""" + from rdkit import Chem + from rdkit.Chem import Descriptors + return len(Descriptors.CalcMolDescriptors(Chem.MolFromSmiles("CCO"))) + + +_DESC_DIM = _infer_desc_dim() # 本机 = 210 + DEFAULT_INPUT_DIMS = { # Channel A: 化学特征 - "mpnn": 600, # D-MPNN embedding - "morgan": 1024, # Morgan fingerprint - "maccs": 167, # MACCS keys - "desc": 217, # RDKit descriptors + "mpnn": 600, # D-MPNN embedding + "morgan": 1024, # Morgan fingerprint + "maccs": 167, # MACCS keys + "desc": _DESC_DIM, # RDKit descriptors（随版本动态） # Channel B: 配方/实验条件 - "comp": 5, # 配方比例 - "phys": 12, # 物理参数 one-hot - "help": 4, # Helper lipid one-hot - "exp": 32, # 实验条件 one-hot + "comp": 5, # 配方比例 + "phys": 12, # 物理参数 one-hot + "help": 4, # Helper lipid one-hot + "exp": 32, # 实验条件 one-hot } # 化学 / 配方 token 的键顺序 @@ -95,6 +104,8 @@ class LNPModel(nn.Module): llm_model_path: str = DEFAULT_MOLT5_PATH, llm_freeze: bool = True, llm_use_lora: bool = False, + llm_use_qlora: bool = False, + use_soft_prompt: bool = False, llm_lora_r: int = 8, llm_lora_alpha: int = 16, llm_lora_dropout: float = 0.05, @@ -171,11 +182,13 @@ class LNPModel(nn.Module): model_name_or_path=llm_model_path, freeze=llm_freeze, use_lora=llm_use_lora, + use_qlora=llm_use_qlora, lora_r=llm_lora_r, lora_alpha=llm_lora_alpha, lora_dropout=llm_lora_dropout, use_rag=use_rag, rag_top_k=rag_top_k, + use_soft_prompt=use_soft_prompt, ) else: self.llm_prompt = None @@ -246,7 +259,10 @@ class LNPModel(nn.Module): f_llm = None if self.llm_prompt is not None and smiles is not None: - f_llm = self.llm_prompt(smiles) + if getattr(self.llm_prompt, "use_soft_prompt", False): + f_llm = self.llm_prompt(smiles, chem=chem, tab=tab) + else: + f_llm = self.llm_prompt(smiles) f_retr = None if self.retr_proj is not None and self.retriever is not None and smiles is not None: @@ -432,6 +448,8 @@ class LNPModelWithoutMPNN(LNPModel): llm_model_path: str = DEFAULT_MOLT5_PATH, llm_freeze: bool = True, llm_use_lora: bool = False, + llm_use_qlora: bool = False, + use_soft_prompt: bool = False, llm_lora_r: int = 8, llm_lora_alpha: int = 16, llm_lora_dropout: float = 0.05, @@ -467,6 +485,8 @@ class LNPModelWithoutMPNN(LNPModel): llm_model_path=llm_model_path, llm_freeze=llm_freeze, llm_use_lora=llm_use_lora, + llm_use_qlora=llm_use_qlora, + use_soft_prompt=use_soft_prompt, llm_lora_r=llm_lora_r, llm_lora_alpha=llm_lora_alpha, llm_lora_dropout=llm_lora_dropout, diff --git a/lnp_ml/modeling/nested_cv_optuna.py b/lnp_ml/modeling/nested_cv_optuna.py index b235d10..56b1d36 100644 --- a/lnp_ml/modeling/nested_cv_optuna.py +++ b/lnp_ml/modeling/nested_cv_optuna.py @@ -246,6 +246,26 @@ def create_model( ) +def _build_rag_pool(full_dataset, idx): + """从 dataset + 索引构造 RAG 池：返回 (smiles, delivery[N], extra_labels)。""" + import numpy as np + idx = np.asarray(idx) + smiles = [full_dataset.smiles[i] for i in idx] + delivery = full_dataset.delivery[idx].reshape(-1) + extra = {} + if full_dataset.size is not None: + extra["size"] = (full_dataset.size[idx], ~np.isnan(full_dataset.size[idx])) + if full_dataset.pdi is not None: + extra["pdi"] = (full_dataset.pdi[idx], full_dataset.pdi_valid[idx]) + if full_dataset.ee is not None: + extra["ee"] = (full_dataset.ee[idx], full_dataset.ee_valid[idx]) + if full_dataset.toxic is not None: + extra["toxic"] = (full_dataset.toxic[idx], full_dataset.toxic[idx] >= 0) + if full_dataset.biodist is not None: + extra["biodist"] = (full_dataset.biodist[idx], full_dataset.biodist_valid[idx]) + return smiles, delivery, extra + + # ============ 评估指标 ============ def evaluate_on_test( @@ -496,7 +516,8 @@ def run_inner_optuna( val_subset = Subset(full_dataset, actual_val_idx.tolist()) train_loader = DataLoader( - train_subset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn + train_subset, batch_size=batch_size, shuffle=True, + collate_fn=collate_fn, drop_last=True, ) val_loader = DataLoader( val_subset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn @@ -525,6 +546,12 @@ def run_inner_optuna( ) if rdkit_cache is not None: model.rdkit_encoder._cache = rdkit_cache + + # 内层 RAG 池：只用内层训练集（防 inner-val 泄漏） + if llm_kwargs_t.get("use_rag", False) and getattr(model, "llm_prompt", None) is not None: + _s, _d, _ex = _build_rag_pool(full_dataset, actual_train_idx) + model.llm_prompt.set_retrieval_pool( + _s, _d, pool_id=f"inner{inner_fold}", extra_labels=_ex) # 加载预训练权重 if pretrain_state_dict is not None and pretrain_config is not None: @@ -631,6 +658,29 @@ def _run_single_outer_fold( fold_dir = Path(fold_dir) fold_dir.mkdir(parents=True, exist_ok=True) + # ===== 断点续跑 ===== + _f_metrics = fold_dir / "test_metrics.json" + _f_bp = fold_dir / "best_params.json" + _f_em = fold_dir / "epoch_mean.json" + # (1) 整折已完成 → 读回结果直接跳过 + if _f_metrics.exists() and _f_bp.exists() and _f_em.exists(): + logger.info(f"[RESUME] Outer fold {outer_fold} 已完成，跳过。") + with open(_f_metrics) as f: + _tm = json.load(f) + with open(_f_bp) as f: + _bp = json.load(f) + with open(_f_em) as f: + _em = json.load(f)["epoch_mean"] + return {"fold": outer_fold, "best_params": _bp, + "epoch_mean": _em, "test_metrics": _tm} + # (2) 内层已完成、外层中断 → 复用超参，跳过内层 Optuna + if precomputed_best_params is None and _f_bp.exists() and _f_em.exists(): + with open(_f_bp) as f: + precomputed_best_params = json.load(f) + with open(_f_em) as f: + precomputed_epoch_mean = json.load(f)["epoch_mean"] + logger.info(f"[RESUME] fold {outer_fold} 复用已存 best_params，跳过内层 Optuna。") + full_dataset = LNPDataset(df) logger.info(f"\n{'='*60}") @@ -703,7 +753,8 @@ def _run_single_outer_fold( test_subset = Subset(full_dataset, outer_test_idx.tolist()) train_loader = DataLoader( - train_subset, batch_size=batch_size, shuffle=True, collate_fn=collate_fn + train_subset, batch_size=batch_size, shuffle=True, + collate_fn=collate_fn, drop_last=True, ) monitor_loader = DataLoader( monitor_subset, batch_size=batch_size, shuffle=False, collate_fn=collate_fn @@ -748,11 +799,10 @@ def _run_single_outer_fold( # ===== RAG 检索池设置（防泄漏：只用训练集分子+标签）===== if llm_kwargs and llm_kwargs.get("use_rag", False) and getattr(model, "llm_prompt", None) is not None: - _rag_smiles = [full_dataset.smiles[i] for i in outer_train_idx] - _rag_labels = full_dataset.delivery[outer_train_idx].reshape(-1) + _s, _d, _ex = _build_rag_pool(full_dataset, outer_train_idx) model.llm_prompt.set_retrieval_pool( - _rag_smiles, _rag_labels, pool_id=f"fold{outer_fold}") - logger.info(f"[RAG] 检索池={len(_rag_smiles)} 训练分子（不含测试集，防泄漏）") + _s, _d, pool_id=f"fold{outer_fold}", extra_labels=_ex) + logger.info(f"[RAG] 检索池={len(_s)} 训练分子（多任务标签，不含测试集，防泄漏）") if pretrain_state_dict is not None and pretrain_config is not None: loaded = load_pretrain_weights_to_model( @@ -776,7 +826,7 @@ def _run_single_outer_fold( freeze_backbone_epochs=3, ) - model.load_state_dict(train_result["final_state"]) + model.load_state_dict(train_result["final_state"], strict=False) model = model.to(device) config = { @@ -838,6 +888,7 @@ def _run_single_outer_fold( def main( input_path: Path = INTERIM_DATA_DIR / "internal.csv", output_dir: Path = MODELS_DIR / "nested_cv", + resume_dir: Optional[Path] = None, # CV 参数 n_outer_folds: int = 5, n_inner_folds: int = 3, @@ -873,6 +924,8 @@ def main( llm_model_path: str = DEFAULT_MOLT5_PATH, llm_freeze: bool = True, llm_use_lora: bool = False, + llm_use_qlora: bool = False, + use_soft_prompt: bool = False, llm_lora_r: int = 8, llm_lora_alpha: int = 16, llm_lora_dropout: float = 0.05, @@ -905,6 +958,8 @@ def main( llm_model_path=llm_model_path, llm_freeze=llm_freeze, llm_use_lora=llm_use_lora, + llm_use_qlora=llm_use_qlora, + use_soft_prompt=use_soft_prompt, llm_lora_r=llm_lora_r, llm_lora_alpha=llm_lora_alpha, llm_lora_dropout=llm_lora_dropout, @@ -923,10 +978,15 @@ def main( 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) + # 创建输出目录（带时间戳；--resume-dir 指定则复用，支持断点续跑） + if resume_dir is not None: + run_dir = Path(resume_dir) + run_dir.mkdir(parents=True, exist_ok=True) + logger.info(f"[RESUME] 复用已有运行目录: {run_dir}") + else: + 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 diff --git a/lnp_ml/modeling/retrieval.py b/lnp_ml/modeling/retrieval.py new file mode 100644 index 0000000..b3cfbc4 --- /dev/null +++ b/lnp_ml/modeling/retrieval.py @@ -0,0 +1,85 @@ +"""Morgan 指纹检索：RAG prompt 构造与检索旁路共用。 + +- _smiles_to_fp: SMILES -> RDKit ExplicitBitVect（供 Tanimoto 相似度）。 +- MorganRetriever: 训练集指纹检索器，query_batch 返回每个查询的 3 维检索特征。 +""" + +from typing import List, Optional + +import numpy as np +from rdkit import Chem, DataStructs +from rdkit.Chem import AllChem + +# 与 RDKitFeaturizer 的 morgan token 保持一致 +MORGAN_RADIUS = 2 +MORGAN_NBITS = 1024 + + +def _smiles_to_fp(smiles: Optional[str], radius: int = MORGAN_RADIUS, n_bits: int = MORGAN_NBITS): + """SMILES -> Morgan ExplicitBitVect；非法/空返回 None。""" + if not smiles: + return None + mol = Chem.MolFromSmiles(smiles) + if mol is None: + return None + return AllChem.GetMorganFingerprintAsBitVect(mol, radius=radius, nBits=n_bits) + + +class MorganRetriever: + """基于 Morgan + Tanimoto 的训练集检索器（用于 use_retrieval 旁路）。 + + query_batch 为每个查询返回 3 维特征： + [相似度加权邻居标签均值, top1 相似度, top-k 平均相似度] + """ + + def __init__(self, smiles_list: List[str], labels, k: int = 5) -> None: + self.k = int(k) + self.pool_smiles = list(smiles_list) + + labels = np.asarray(labels, dtype=np.float32) + if labels.ndim == 1: + labels = labels.reshape(-1, 1) + self.pool_labels = labels # [N, D]，旁路只用第 0 列 + + fps = [_smiles_to_fp(s) for s in self.pool_smiles] + # 只保留可解析的池分子 + self._valid_pos = [i for i, fp in enumerate(fps) if fp is not None] + self._valid_fps = [fps[i] for i in self._valid_pos] + self._valid_labels = self.pool_labels[self._valid_pos, 0] if self._valid_pos else np.zeros(0, np.float32) + self._valid_smiles = [self.pool_smiles[i] for i in self._valid_pos] + self._fp_cache = {} # query 指纹缓存 + + def _query_fp(self, smiles: str): + if smiles not in self._fp_cache: + self._fp_cache[smiles] = _smiles_to_fp(smiles) + return self._fp_cache[smiles] + + def query_batch(self, smiles_list: List[str], exclude_self="auto") -> np.ndarray: + return np.stack([self._query_one(s, exclude_self) for s in smiles_list]).astype(np.float32) + + def _query_one(self, smiles: str, exclude_self) -> np.ndarray: + qfp = self._query_fp(smiles) + if qfp is None or not self._valid_fps: + return np.zeros(3, dtype=np.float32) + + # 向量化 Tanimoto（C 层批量），替代 Python 逐条 + sims = np.asarray(DataStructs.BulkTanimotoSimilarity(qfp, self._valid_fps), dtype=np.float32) + + if exclude_self: + self_mask = np.fromiter((s == smiles for s in self._valid_smiles), dtype=bool, count=len(sims)) + sims = np.where(self_mask, -1.0, sims) + + k = min(self.k, int((sims >= 0).sum())) + if k <= 0: + return np.zeros(3, dtype=np.float32) + + # argpartition 取 top-k（O(N)），再对这 k 个排序 + top = np.argpartition(-sims, k - 1)[:k] + top = top[np.argsort(-sims[top])] + top_sims = sims[top] + top_labels = self._valid_labels[top] + + w = np.clip(top_sims, 0.0, None) + wsum = float(w.sum()) + weighted_label = float((w * top_labels).sum() / wsum) if wsum > 0 else float(top_labels.mean()) + return np.array([weighted_label, float(top_sims[0]), float(top_sims.mean())], dtype=np.float32) \ No newline at end of file diff --git a/lnp_ml/modeling/trainer_balanced.py b/lnp_ml/modeling/trainer_balanced.py index 407e4a2..a54ef58 100644 --- a/lnp_ml/modeling/trainer_balanced.py +++ b/lnp_ml/modeling/trainer_balanced.py @@ -406,7 +406,7 @@ def train_with_early_stopping( scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( optimizer, mode="min", factor=0.5, patience=5 ) - early_stopping = EarlyStoppingBalanced(patience=patience) + early_stopping = EarlyStoppingBalanced(patience=patience, min_delta=1e-3) history = {"train": [], "val": []} best_val_loss = float("inf") @@ -438,9 +438,9 @@ def train_with_early_stopping( if early_stopping(val_metrics["loss"], epoch): break - # Restore best model + # Restore best model（QLoRA 4-bit 基座的量化元数据键用 strict=False 忽略） if best_state is not None: - model.load_state_dict(best_state) + model.load_state_dict(best_state, strict=False) return { "history": history, 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 deleted file mode 100644 index 3e2c1aa..0000000 --- a/models/abl/baseline/20260617_180556/outer_fold_0/best_params.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "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/test_metrics.json b/models/abl/baseline/20260617_180556/outer_fold_0/test_metrics.json deleted file mode 100644 index f809c65..0000000 --- a/models/abl/baseline/20260617_180556/outer_fold_0/test_metrics.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "size": { - "n_samples": 83, - "mse": 0.32790454280151365, - "rmse": 0.572629498717551, - "mae": 0.35871042688208893, - "r2": -0.08876073797082196 - }, - "delivery": { - "n_samples": 58, - "mse": 0.7250728333301815, - "rmse": 0.8515120864263651, - "mae": 0.7098816664696768, - "r2": 0.07838867510225656 - }, - "pdi": { - "n_samples": 84, - "accuracy": 0.5476190476190477, - "precision": 0.3435971685971686, - "recall": 0.5359094457455114, - "f1": 0.34977324263038545 - }, - "ee": { - "n_samples": 84, - "accuracy": 0.7142857142857143, - "precision": 0.6871101871101871, - "recall": 0.707142857142857, - "f1": 0.6756066411238826 - }, - "toxic": { - "n_samples": 58, - "accuracy": 0.9655172413793104, - "precision": 0.75, - "recall": 0.9821428571428572, - "f1": 0.8242424242424242 - }, - "biodist": { - "n_samples": 58, - "kl_divergence": 0.4073394583325288, - "js_divergence": 0.092887269703335 - } -} \ No newline at end of file diff --git a/models/abl/baseline/20260617_180556/outer_fold_1/best_params.json b/models/abl/baseline/20260617_180556/outer_fold_1/best_params.json deleted file mode 100644 index b8d0ca3..0000000 --- a/models/abl/baseline/20260617_180556/outer_fold_1/best_params.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "dropout": 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0.9586175198423679, - "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_2/test_metrics.json b/models/abl/baseline/20260617_180556/outer_fold_2/test_metrics.json deleted file mode 100644 index 2a51f0f..0000000 --- a/models/abl/baseline/20260617_180556/outer_fold_2/test_metrics.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "size": { - "n_samples": 84, - "mse": 0.07870736214858826, - "rmse": 0.28054832408800495, - "mae": 0.2252055633635748, - "r2": 0.15667568332606574 - }, - "delivery": { - "n_samples": 60, - "mse": 0.6624288051399898, - "rmse": 0.8138972939750997, - "mae": 0.5578871982870623, - "r2": 0.118192445613993 - }, - "pdi": { - "n_samples": 84, - "accuracy": 0.5714285714285714, - "precision": 0.43453195231581004, - "recall": 0.7014848950332823, - "f1": 0.4452380952380952 - }, - "ee": { - 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- "head_hidden_dim": 128, - "set_transformer_block": "sab" -} \ No newline at end of file diff --git a/models/abl/baseline/20260617_180556/outer_fold_3/test_metrics.json b/models/abl/baseline/20260617_180556/outer_fold_3/test_metrics.json deleted file mode 100644 index 5b4cdb8..0000000 --- a/models/abl/baseline/20260617_180556/outer_fold_3/test_metrics.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "size": { - "n_samples": 83, - "mse": 0.477705869562307, - "rmse": 0.6911626939891266, - "mae": 0.38659381004701177, - "r2": -0.46585807501413723 - }, - "delivery": { - "n_samples": 59, - "mse": 0.7692103143544243, - "rmse": 0.8770463581558413, - "mae": 0.6155587352528158, - "r2": 0.2522361838732067 - }, - "pdi": { - "n_samples": 84, - "accuracy": 0.6904761904761905, - "precision": 0.4426767676767677, - "recall": 0.6817716333845366, - "f1": 0.43613724611708476 - }, - "ee": { - "n_samples": 84, - "accuracy": 0.6428571428571429, - "precision": 0.5650987224157956, - "recall": 0.5790095525389644, - 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a/models/abl/baseline/20260617_180556/outer_fold_4/test_metrics.json b/models/abl/baseline/20260617_180556/outer_fold_4/test_metrics.json deleted file mode 100644 index e553d93..0000000 --- a/models/abl/baseline/20260617_180556/outer_fold_4/test_metrics.json +++ /dev/null @@ -1,42 +0,0 @@ -{ - "size": { - "n_samples": 84, - "mse": 0.1842198466764517, - "rmse": 0.4292083953937198, - "mae": 0.29178874833243235, - "r2": -0.33736192476816806 - }, - "delivery": { - "n_samples": 58, - "mse": 0.9003281069383875, - "rmse": 0.9488562098328637, - "mae": 0.6070586243837044, - "r2": 0.1293975832987566 - }, - "pdi": { - "n_samples": 84, - "accuracy": 0.5476190476190477, - "precision": 0.34548450889914306, - "recall": 0.5337941628264209, - "f1": 0.3497882046269143 - }, - "ee": { - "n_samples": 84, - "accuracy": 0.7023809523809523, - "precision": 0.6548765244417418, - "recall": 0.7576923076923077, - "f1": 0.6777508856852221 - }, - "toxic": { - "n_samples": 59, - "accuracy": 0.9661016949152542, - 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b/models/abl/baseline/20260617_180556/repeat_1/outer_fold_0/splits.json deleted file mode 100644 index cccb9d8..0000000 --- a/models/abl/baseline/20260617_180556/repeat_1/outer_fold_0/splits.json +++ /dev/null @@ -1 +0,0 @@ -{"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, 44, 45, 46, 47, 48, 49, 50, 52, 53, 54, 55, 56, 57, 58, 61, 62, 63, 64, 65, 66, 67, 68, 71, 72, 73, 75, 76, 77, 80, 81, 85, 86, 88, 89, 90, 92, 94, 95, 96, 98, 99, 100, 101, 103, 104, 105, 106, 107, 108, 109, 111, 113, 114, 115, 116, 117, 118, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 133, 134, 135, 136, 138, 139, 141, 144, 145, 146, 147, 148, 149, 150, 151, 153, 154, 155, 156, 157, 158, 159, 160, 163, 164, 167, 168, 169, 170, 171, 172, 175, 176, 178, 179, 180, 182, 183, 184, 185, 188, 189, 190, 191, 192, 193, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 206, 208, 210, 211, 212, 213, 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a/models/abl/baseline/20260617_180556/repeat_2/outer_fold_0/best_params.json b/models/abl/baseline/20260617_180556/repeat_2/outer_fold_0/best_params.json deleted file mode 100644 index 3e2c1aa..0000000 --- a/models/abl/baseline/20260617_180556/repeat_2/outer_fold_0/best_params.json +++ /dev/null @@ -1,12 +0,0 @@ -{ - "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/repeat_2/outer_fold_0/splits.json b/models/abl/baseline/20260617_180556/repeat_2/outer_fold_0/splits.json deleted file mode 100644 index cccb9d8..0000000 --- a/models/abl/baseline/20260617_180556/repeat_2/outer_fold_0/splits.json +++ /dev/null @@ -1 +0,0 @@ -{"outer_train_idx": [0, 1, 2, 3, 4, 5, 6, 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a/models/abl/llm/20260617_203747/repeat_1/outer_fold_3/best_params.json b/models/abl/llm/20260617_203747/repeat_1/outer_fold_3/best_params.json deleted file mode 100644 index 5b8f4f2..0000000 --- a/models/abl/llm/20260617_203747/repeat_1/outer_fold_3/best_params.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "dropout": 0.26162738320409185, - "lr": 0.0004044490597558798, - "weight_decay": 7.205221534692395e-05, - "backbone_lr_ratio": 0.07900176413772968, - "llm_lora_r": 32, - "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/llm/20260617_203747/repeat_1/outer_fold_3/splits.json b/models/abl/llm/20260617_203747/repeat_1/outer_fold_3/splits.json deleted file mode 100644 index 7a2b13e..0000000 --- a/models/abl/llm/20260617_203747/repeat_1/outer_fold_3/splits.json +++ /dev/null @@ -1 +0,0 @@ -{"outer_train_idx": [0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 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a/models/abl/baseline/20260617_180556/outer_fold_1/splits.json b/models/abl_qwen_softrag/seed42/outer_fold_1/splits.json similarity index 100% rename from models/abl/baseline/20260617_180556/outer_fold_1/splits.json rename to models/abl_qwen_softrag/seed42/outer_fold_1/splits.json diff --git a/models/abl_qwen_softrag/seed42/outer_fold_1/test_metrics.json b/models/abl_qwen_softrag/seed42/outer_fold_1/test_metrics.json new file mode 100644 index 0000000..e8c5765 --- /dev/null +++ b/models/abl_qwen_softrag/seed42/outer_fold_1/test_metrics.json @@ -0,0 +1,42 @@ +{ + "size": { + "n_samples": 84, + "mse": 0.30248181045202543, + "rmse": 0.5499834637987087, + "mae": 0.41426232776471544, + "r2": 0.07632809002212781 + }, + "delivery": { + "n_samples": 61, + "mse": 0.9428140250706672, + "rmse": 0.9709861096177779, + "mae": 0.6117104583526732, + "r2": 0.28635774947742054 + }, + "pdi": { + "n_samples": 84, + "accuracy": 0.7142857142857143, + "precision": 0.6352542372881356, + "recall": 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"[$(date)] START seed=${SEED} gpu=${GPU} batch=${BATCH} dir=${RUN_DIR}" + +CUDA_VISIBLE_DEVICES=${GPU} python -m lnp_ml.modeling.nested_cv_optuna \ + --input-path data/interim/internal.csv \ + --output-dir models/abl_qwen_softrag \ + --resume-dir "${RUN_DIR}" \ + --seed ${SEED} \ + --use-llm --use-rag --rag-top-k 4 \ + --use-soft-prompt --llm-use-qlora \ + --llm-model-path models/qwen2.5-7b-instruct \ + --no-llm-freeze \ + --n-outer-folds ${N_OUTER} --n-inner-folds ${N_INNER} \ + --n-trials ${N_TRIALS} --epochs-per-trial ${EPOCHS} \ + --inner-patience ${PATIENCE} \ + --batch-size ${BATCH} --device cuda \ + 2>&1 | tee -a "${RUN_DIR}/run.log" + +echo "[$(date)] DONE seed=${SEED} (exit=${PIPESTATUS[0]})" \ No newline at end of file