mirror of
https://github.com/RYDE-WORK/lnp_ml.git
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Merge branch 'feat/moe-layer' of github-michelle:RYDE-WORK/lnp_ml into feat/moe-layer
# Conflicts: # lnp_ml/modeling/layers/llm_prompt.py
This commit is contained in:
Michelle0474
commit
182f7853d7
9
.gitignore
vendored
9
.gitignore
vendored
@ -188,4 +188,11 @@ cython_debug/
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.ruff_cache/
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|
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# PyPI configuration file
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.pypirc
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.pypirc
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|
||||
logs/
|
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*.out
|
||||
models/**/*.pt
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models/**/*.png
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models/molt5-base/
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models/abl/
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1071
lnp_ml/dataset.py
1071
lnp_ml/dataset.py
File diff suppressed because it is too large
Load Diff
@ -1,118 +1,125 @@
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import torch
|
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import torch.nn as nn
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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),
|
||||
)
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||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
"""[B, in_dim] -> [B, 1]"""
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||||
return self.net(x)
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||||
|
||||
|
||||
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
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||||
) -> 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, 2] 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)
|
||||
size_dropout = min(0.5, dropout + 0.2)
|
||||
self.size_head = RegressionHead(in_dim, hidden_dim, size_dropout)
|
||||
|
||||
# PDI: 2 分类
|
||||
self.pdi_head = ClassificationHead(in_dim, num_classes=2, 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, 2] 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),
|
||||
}
|
||||
|
||||
@ -1,6 +1,16 @@
|
||||
from lnp_ml.modeling.layers.token_projector import TokenProjector
|
||||
from lnp_ml.modeling.layers.bidirectional_cross_attention import CrossModalAttention
|
||||
from lnp_ml.modeling.layers.fusion import FusionLayer
|
||||
from lnp_ml.modeling.layers.set_transformer import SetTransformer
|
||||
from lnp_ml.modeling.layers.fusion import FusionLayer, ResidualConcatFusion
|
||||
from lnp_ml.modeling.layers.moe import MoEBlock
|
||||
from lnp_ml.modeling.layers.llm_prompt import LLMPromptEncoder
|
||||
|
||||
__all__ = ["TokenProjector", "CrossModalAttention", "FusionLayer", "MoEBlock"]
|
||||
__all__ = [
|
||||
"TokenProjector",
|
||||
"CrossModalAttention",
|
||||
"SetTransformer",
|
||||
"FusionLayer",
|
||||
"ResidualConcatFusion",
|
||||
"MoEBlock",
|
||||
"LLMPromptEncoder",
|
||||
]
|
||||
@ -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
|
||||
@ -1,104 +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:
|
||||
"""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()
|
||||
123
lnp_ml/modeling/layers/set_transformer.py
Normal file
123
lnp_ml/modeling/layers/set_transformer.py
Normal file
@ -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
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
0
lnp_ml/utils/__init__.py
Normal file
0
lnp_ml/utils/__init__.py
Normal file
19
lnp_ml/utils/seed.py
Normal file
19
lnp_ml/utils/seed.py
Normal file
@ -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)
|
||||
@ -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"
|
||||
}
|
||||
@ -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, 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, 216, 217, 218, 219, 220, 222, 224, 225, 227, 228, 229, 230, 231, 232, 233, 234, 236, 237, 239, 241, 242, 244, 245, 247, 248, 249, 250, 252, 253, 255, 256, 257, 258, 259, 260, 261, 262, 265, 267, 268, 269, 270, 271, 272, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 285, 286, 287, 288, 291, 292, 294, 296, 297, 298, 299, 300, 301, 303, 304, 306, 307, 308, 309, 310, 311, 312, 313, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 332, 333, 335, 336, 337, 338, 339, 340, 342, 343, 344, 345, 346, 347, 348, 349, 351, 352, 354, 355, 356, 357, 358, 359, 360, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 374, 375, 377, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 393, 395, 396, 397, 398, 399, 400, 401, 403, 404, 405, 406, 407, 408, 409, 411, 412, 413, 414, 415, 417, 418, 419], "outer_test_idx": [7, 16, 20, 29, 51, 59, 60, 69, 70, 74, 78, 79, 82, 83, 84, 87, 91, 93, 97, 102, 110, 112, 119, 120, 121, 132, 137, 140, 142, 143, 152, 161, 162, 165, 166, 173, 174, 177, 181, 186, 187, 194, 205, 207, 209, 214, 215, 221, 223, 226, 235, 238, 240, 243, 246, 251, 254, 263, 264, 266, 273, 284, 289, 290, 293, 295, 302, 305, 314, 315, 331, 334, 341, 350, 353, 361, 373, 376, 378, 392, 394, 402, 410, 416]}
|
||||
@ -0,0 +1,42 @@
|
||||
{
|
||||
"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
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,12 @@
|
||||
{
|
||||
"dropout": 0.23085562232445592,
|
||||
"lr": 0.0005227270589225132,
|
||||
"weight_decay": 0.00461701039547356,
|
||||
"backbone_lr_ratio": 0.12087164274488635,
|
||||
"d_model": 256,
|
||||
"num_heads": 8,
|
||||
"n_attn_layers": 4,
|
||||
"fusion_strategy": "attention",
|
||||
"head_hidden_dim": 128,
|
||||
"set_transformer_block": "sab"
|
||||
}
|
||||
@ -0,0 +1 @@
|
||||
{"outer_train_idx": [0, 3, 5, 6, 7, 10, 11, 12, 13, 15, 16, 18, 19, 20, 21, 24, 26, 27, 29, 30, 31, 32, 33, 35, 36, 38, 39, 40, 42, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 65, 66, 67, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 97, 98, 99, 100, 101, 102, 103, 105, 106, 107, 108, 109, 110, 112, 114, 115, 118, 119, 120, 121, 123, 124, 125, 126, 128, 129, 130, 131, 132, 136, 137, 140, 141, 142, 143, 144, 145, 146, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 161, 162, 164, 165, 166, 167, 168, 169, 171, 172, 173, 174, 175, 176, 177, 178, 179, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 194, 195, 196, 197, 198, 199, 201, 202, 204, 205, 206, 207, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 225, 226, 228, 229, 231, 232, 233, 234, 235, 236, 237, 238, 240, 242, 243, 244, 246, 247, 248, 250, 251, 252, 254, 255, 256, 258, 259, 260, 261, 262, 263, 264, 266, 268, 269, 270, 271, 273, 274, 275, 276, 277, 278, 279, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 295, 296, 297, 300, 301, 302, 303, 304, 305, 306, 307, 308, 310, 311, 313, 314, 315, 316, 317, 318, 319, 321, 322, 323, 324, 325, 327, 328, 329, 330, 331, 333, 334, 335, 337, 338, 339, 340, 341, 342, 343, 344, 345, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 361, 362, 364, 365, 366, 367, 368, 369, 373, 374, 375, 376, 378, 379, 380, 381, 382, 384, 387, 388, 389, 391, 392, 393, 394, 396, 397, 398, 399, 402, 403, 404, 405, 406, 408, 409, 410, 411, 412, 413, 414, 416, 417, 419], "outer_test_idx": [1, 2, 4, 8, 9, 14, 17, 22, 23, 25, 28, 34, 37, 41, 43, 45, 63, 64, 68, 85, 96, 104, 111, 113, 116, 117, 122, 127, 133, 134, 135, 138, 139, 147, 160, 163, 170, 180, 191, 192, 193, 200, 203, 208, 224, 227, 230, 239, 241, 245, 249, 253, 257, 265, 267, 272, 280, 294, 298, 299, 309, 312, 320, 326, 332, 336, 346, 347, 360, 363, 370, 371, 372, 377, 383, 385, 386, 390, 395, 400, 401, 407, 415, 418]}
|
||||
@ -0,0 +1,42 @@
|
||||
{
|
||||
"size": {
|
||||
"n_samples": 84,
|
||||
"mse": 0.233876145855699,
|
||||
"rmse": 0.4836074294876982,
|
||||
"mae": 0.3760785318556286,
|
||||
"r2": -2.874543407510119
|
||||
},
|
||||
"delivery": {
|
||||
"n_samples": 61,
|
||||
"mse": 1.0238964615450643,
|
||||
"rmse": 1.0118776910007772,
|
||||
"mae": 0.6578027546466862,
|
||||
"r2": 0.2249841902125319
|
||||
},
|
||||
"pdi": {
|
||||
"n_samples": 84,
|
||||
"accuracy": 0.5238095238095238,
|
||||
"precision": 0.34497354497354493,
|
||||
"recall": 0.3280423280423281,
|
||||
"f1": 0.3231922398589065
|
||||
},
|
||||
"ee": {
|
||||
"n_samples": 84,
|
||||
"accuracy": 0.5595238095238095,
|
||||
"precision": 0.5101731601731602,
|
||||
"recall": 0.5036154321868608,
|
||||
"f1": 0.48790324563862697
|
||||
},
|
||||
"toxic": {
|
||||
"n_samples": 61,
|
||||
"accuracy": 0.9508196721311475,
|
||||
"precision": 0.75,
|
||||
"recall": 0.9741379310344828,
|
||||
"f1": 0.8200589970501475
|
||||
},
|
||||
"biodist": {
|
||||
"n_samples": 61,
|
||||
"kl_divergence": 0.48528378254405913,
|
||||
"js_divergence": 0.12258377401744426
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,12 @@
|
||||
{
|
||||
"dropout": 0.18117503720006686,
|
||||
"lr": 8.135394049241399e-05,
|
||||
"weight_decay": 0.015076469897649124,
|
||||
"backbone_lr_ratio": 0.9586175198423679,
|
||||
"d_model": 256,
|
||||
"num_heads": 8,
|
||||
"n_attn_layers": 4,
|
||||
"fusion_strategy": "attention",
|
||||
"head_hidden_dim": 128,
|
||||
"set_transformer_block": "sab"
|
||||
}
|
||||
@ -0,0 +1 @@
|
||||
{"outer_train_idx": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 32, 33, 34, 36, 37, 38, 39, 40, 41, 43, 44, 45, 47, 48, 49, 50, 51, 55, 56, 57, 58, 59, 60, 61, 63, 64, 65, 68, 69, 70, 72, 74, 76, 78, 79, 80, 82, 83, 84, 85, 86, 87, 88, 91, 93, 94, 95, 96, 97, 100, 101, 102, 103, 104, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 142, 143, 145, 147, 148, 149, 150, 151, 152, 153, 156, 157, 158, 159, 160, 161, 162, 163, 165, 166, 167, 168, 170, 171, 172, 173, 174, 175, 177, 179, 180, 181, 183, 184, 185, 186, 187, 188, 191, 192, 193, 194, 196, 197, 199, 200, 202, 203, 204, 205, 207, 208, 209, 210, 211, 212, 213, 214, 215, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 232, 233, 235, 236, 237, 238, 239, 240, 241, 242, 243, 245, 246, 249, 250, 251, 252, 253, 254, 255, 256, 257, 259, 260, 261, 263, 264, 265, 266, 267, 268, 269, 271, 272, 273, 274, 275, 276, 280, 281, 282, 283, 284, 285, 287, 289, 290, 293, 294, 295, 296, 297, 298, 299, 300, 302, 303, 305, 307, 308, 309, 310, 311, 312, 313, 314, 315, 317, 318, 320, 321, 322, 323, 324, 326, 327, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 358, 360, 361, 363, 364, 365, 366, 368, 369, 370, 371, 372, 373, 376, 377, 378, 379, 380, 381, 382, 383, 385, 386, 387, 388, 389, 390, 392, 394, 395, 398, 399, 400, 401, 402, 404, 405, 407, 408, 409, 410, 412, 413, 414, 415, 416, 417, 418], "outer_test_idx": [10, 12, 15, 31, 35, 42, 46, 52, 53, 54, 62, 66, 67, 71, 73, 75, 77, 81, 89, 90, 92, 98, 99, 105, 123, 124, 141, 144, 146, 154, 155, 164, 169, 176, 178, 182, 189, 190, 195, 198, 201, 206, 216, 217, 218, 219, 231, 234, 244, 247, 248, 258, 262, 270, 277, 278, 279, 286, 288, 291, 292, 301, 304, 306, 316, 319, 325, 328, 343, 357, 359, 362, 367, 374, 375, 384, 391, 393, 396, 397, 403, 406, 411, 419]}
|
||||
@ -0,0 +1,42 @@
|
||||
{
|
||||
"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": {
|
||||
"n_samples": 84,
|
||||
"accuracy": 0.6666666666666666,
|
||||
"precision": 0.6140109890109889,
|
||||
"recall": 0.6785714285714285,
|
||||
"f1": 0.6262626262626263
|
||||
},
|
||||
"toxic": {
|
||||
"n_samples": 61,
|
||||
"accuracy": 0.9344262295081968,
|
||||
"precision": 0.7142857142857143,
|
||||
"recall": 0.9655172413793103,
|
||||
"f1": 0.7821428571428571
|
||||
},
|
||||
"biodist": {
|
||||
"n_samples": 60,
|
||||
"kl_divergence": 0.3967825471495568,
|
||||
"js_divergence": 0.1057568924841401
|
||||
}
|
||||
}
|
||||
@ -0,0 +1,12 @@
|
||||
{
|
||||
"dropout": 0.1799936812783694,
|
||||
"lr": 0.00033865324369584493,
|
||||
"weight_decay": 0.01440133094015265,
|
||||
"backbone_lr_ratio": 0.3278334190266755,
|
||||
"d_model": 256,
|
||||
"num_heads": 8,
|
||||
"n_attn_layers": 4,
|
||||
"fusion_strategy": "attention",
|
||||
"head_hidden_dim": 128,
|
||||
"set_transformer_block": "sab"
|
||||
}
|
||||
@ -0,0 +1 @@
|
||||
{"outer_train_idx": [0, 1, 2, 4, 5, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 32, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 51, 52, 53, 54, 55, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 76, 77, 78, 79, 81, 82, 83, 84, 85, 86, 87, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 102, 104, 105, 106, 109, 110, 111, 112, 113, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 127, 129, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 146, 147, 148, 150, 151, 152, 153, 154, 155, 156, 158, 159, 160, 161, 162, 163, 164, 165, 166, 168, 169, 170, 172, 173, 174, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 189, 190, 191, 192, 193, 194, 195, 198, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 212, 213, 214, 215, 216, 217, 218, 219, 221, 223, 224, 226, 227, 228, 230, 231, 233, 234, 235, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 251, 252, 253, 254, 255, 256, 257, 258, 259, 262, 263, 264, 265, 266, 267, 268, 269, 270, 272, 273, 274, 275, 276, 277, 278, 279, 280, 282, 284, 286, 288, 289, 290, 291, 292, 293, 294, 295, 297, 298, 299, 301, 302, 304, 305, 306, 307, 308, 309, 310, 312, 314, 315, 316, 317, 319, 320, 321, 325, 326, 327, 328, 331, 332, 334, 336, 340, 341, 343, 344, 346, 347, 348, 350, 352, 353, 357, 359, 360, 361, 362, 363, 364, 366, 367, 368, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 382, 383, 384, 385, 386, 387, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 400, 401, 402, 403, 404, 406, 407, 410, 411, 412, 414, 415, 416, 418, 419], "outer_test_idx": [3, 6, 13, 18, 19, 26, 33, 44, 48, 49, 50, 56, 57, 72, 80, 88, 94, 101, 103, 107, 108, 114, 126, 128, 130, 145, 149, 157, 167, 171, 175, 188, 196, 197, 199, 211, 220, 222, 225, 229, 232, 236, 250, 260, 261, 271, 281, 283, 285, 287, 296, 300, 303, 311, 313, 318, 322, 323, 324, 329, 330, 333, 335, 337, 338, 339, 342, 345, 349, 351, 354, 355, 356, 358, 365, 369, 381, 388, 399, 405, 408, 409, 413, 417]}
|
||||
@ -0,0 +1,42 @@
|
||||
{
|
||||
"size": {
|
||||
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|
||||
"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": {
|
||||
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|
||||
"accuracy": 0.6904761904761905,
|
||||
"precision": 0.4426767676767677,
|
||||
"recall": 0.6817716333845366,
|
||||
"f1": 0.43613724611708476
|
||||
},
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60
models/abl/baseline/20260617_180556/strata_info.json
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60
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932
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Normal file
932
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Normal file
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1
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1
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Normal file
@ -0,0 +1 @@
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1
models/abl/both/20260617_232841/outer_fold_1/splits.json
Normal file
1
models/abl/both/20260617_232841/outer_fold_1/splits.json
Normal file
@ -0,0 +1 @@
|
||||
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1
models/abl/both/20260617_232841/outer_fold_2/splits.json
Normal file
1
models/abl/both/20260617_232841/outer_fold_2/splits.json
Normal file
@ -0,0 +1 @@
|
||||
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1
models/abl/both/20260617_232841/outer_fold_3/splits.json
Normal file
1
models/abl/both/20260617_232841/outer_fold_3/splits.json
Normal file
@ -0,0 +1 @@
|
||||
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1
models/abl/both/20260617_232841/outer_fold_4/splits.json
Normal file
1
models/abl/both/20260617_232841/outer_fold_4/splits.json
Normal file
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@ -0,0 +1,42 @@
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@ -0,0 +1,16 @@
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@ -0,0 +1 @@
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@ -0,0 +1,42 @@
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||||
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Reference in New Issue
Block a user