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Graph Neural Network models for retention time prediction.
Includes GCN, GIN, GAT, and ensemble models.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch_geometric.nn import (
GCNConv, GINConv, GATConv, GINEConv, GlobalAttention,
global_mean_pool, global_max_pool, global_add_pool
)
from torch_geometric.nn.norm import BatchNorm, GraphNorm, LayerNorm
from typing import Optional, List, Dict, Sequence, Any
import numpy as np
class GraphConvModel(nn.Module):
"""Base Graph Convolutional Network model."""
def __init__(self, input_dim: int, hidden_dim: int = 128, output_dim: int = 1,
num_layers: int = 3, dropout: float = 0.2,
num_labs: int = 23, lab_embed_dim: int = 16):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.dropout = dropout
# Lab embedding
self.lab_embedding = nn.Embedding(num_labs, lab_embed_dim)
# Graph convolution layers
self.convs = nn.ModuleList()
self.batch_norms = nn.ModuleList()
# First layer
self.convs.append(GCNConv(input_dim, hidden_dim))
self.batch_norms.append(GraphNorm(hidden_dim))
# Hidden layers
for _ in range(num_layers - 1):
self.convs.append(GCNConv(hidden_dim, hidden_dim))
self.batch_norms.append(GraphNorm(hidden_dim))
# Global pooling
self.global_pool = global_mean_pool
# Final prediction layers
final_input_dim = hidden_dim + lab_embed_dim
self.predictor = nn.Sequential(
nn.Linear(final_input_dim, hidden_dim // 2),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim // 2, hidden_dim // 4),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim // 4, output_dim)
)
def forward(self, x, edge_index, batch, lab_feature, edge_attr=None):
# Graph convolutions
for conv, norm in zip(self.convs, self.batch_norms):
# GCN expects edge_weight, not edge_attr for individual edge features
edge_weight = None
if edge_attr is not None and edge_attr.size(1) == 1:
edge_weight = edge_attr.squeeze(-1)
x = conv(x, edge_index, edge_weight=edge_weight)
x = norm(x, batch)
x = F.relu(x)
x = F.dropout(x, p=self.dropout, training=self.training)
# Global pooling
graph_repr = self.global_pool(x, batch)
# Lab embedding - handle different input shapes
if lab_feature.dim() == 1:
lab_embed = self.lab_embedding(lab_feature)
else:
lab_embed = self.lab_embedding(lab_feature.squeeze(-1))
# Concatenate graph and lab features
combined = torch.cat([graph_repr, lab_embed], dim=1)
# Final prediction
output = self.predictor(combined)
return output.squeeze()
class GATModel(nn.Module):
"""Graph Attention Network (GAT) model."""
def __init__(self, input_dim: int, hidden_dim: int = 128, output_dim: int = 1,
num_layers: int = 3, dropout: float = 0.2, num_heads: int = 4,
num_labs: int = 23, lab_embed_dim: int = 16):
super().__init__()
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.dropout = dropout
self.num_heads = num_heads
# Lab embedding
self.lab_embedding = nn.Embedding(num_labs, lab_embed_dim)
# GAT layers
self.convs = nn.ModuleList()
self.batch_norms = nn.ModuleList()
# Calculate dimensions properly for multi-head attention
head_dim = hidden_dim // num_heads
# First layer: input -> hidden_dim (via multi-head)
self.convs.append(GATConv(input_dim, head_dim, heads=num_heads, dropout=dropout, concat=True))
self.batch_norms.append(GraphNorm(hidden_dim)) # head_dim * num_heads = hidden_dim
# Hidden layers: hidden_dim -> hidden_dim (via multi-head)
for _ in range(num_layers - 2):
self.convs.append(GATConv(hidden_dim, head_dim, heads=num_heads, dropout=dropout, concat=True))
self.batch_norms.append(GraphNorm(hidden_dim))
# Last layer: hidden_dim -> hidden_dim (single head)
if num_layers > 1:
self.convs.append(GATConv(hidden_dim, hidden_dim, heads=1, dropout=dropout, concat=False))
self.batch_norms.append(GraphNorm(hidden_dim))
# Global pooling
self.global_pool = global_mean_pool
# Final prediction layers
final_input_dim = hidden_dim + lab_embed_dim
self.predictor = nn.Sequential(
nn.Linear(final_input_dim, hidden_dim // 2),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim // 2, hidden_dim // 4),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim // 4, output_dim)
)
def forward(self, x, edge_index, batch, lab_feature, edge_attr=None):
# GAT convolutions
for i, (conv, norm) in enumerate(zip(self.convs, self.batch_norms)):
x = conv(x, edge_index)
x = norm(x, batch)
if i < len(self.convs) - 1: # No ReLU on last layer
x = F.relu(x)
x = F.dropout(x, p=self.dropout, training=self.training)
# Global pooling
graph_repr = self.global_pool(x, batch)
# Lab embedding - handle different input shapes
if lab_feature.dim() == 1:
lab_embed = self.lab_embedding(lab_feature)
else:
lab_embed = self.lab_embedding(lab_feature.squeeze(-1))
# Concatenate graph and lab features
combined = torch.cat([graph_repr, lab_embed], dim=1)
# Final prediction
output = self.predictor(combined)
return output.squeeze()
class MoleculeMPNNModel(nn.Module):
"""Edge-aware message passing network tailored for molecular graphs."""
def __init__(
self,
input_dim: int,
hidden_dim: int = 256,
output_dim: int = 1,
num_layers: int = 4,
dropout: float = 0.2,
edge_dim: int = 4,
num_labs: int = 23,
lab_embed_dim: int = 16,
use_batch_norm: bool = True,
):
super().__init__()
self.hidden_dim = hidden_dim
self.num_layers = num_layers
self.dropout = dropout
self.use_batch_norm = use_batch_norm
self.input_proj = nn.Linear(input_dim, hidden_dim)
self.edge_encoder = nn.Linear(edge_dim, hidden_dim) if edge_dim > 0 else None
self.lab_embedding = nn.Embedding(num_labs, lab_embed_dim)
self.convs = nn.ModuleList()
self.norms = nn.ModuleList()
for _ in range(num_layers):
mlp = nn.Sequential(
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
)
self.convs.append(GINEConv(mlp, train_eps=True))
if use_batch_norm:
self.norms.append(BatchNorm(hidden_dim))
else:
self.norms.append(GraphNorm(hidden_dim))
pooled_dim = hidden_dim * 2 # mean + max pooling
final_input_dim = pooled_dim + lab_embed_dim
self.predictor = nn.Sequential(
nn.Linear(final_input_dim, hidden_dim),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, hidden_dim // 2),
nn.ReLU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim // 2, output_dim),
)
def forward(self, x, edge_index, batch, lab_feature, edge_attr=None):
x = self.input_proj(x)
encoded_edge_attr = edge_attr
if edge_attr is not None and self.edge_encoder is not None:
encoded_edge_attr = self.edge_encoder(edge_attr)
for conv, norm in zip(self.convs, self.norms):
x = conv(x, edge_index, encoded_edge_attr)
if isinstance(norm, BatchNorm):
x = norm(x)
else:
x = norm(x, batch)
x = F.relu(x)
x = F.dropout(x, p=self.dropout, training=self.training)
mean_pool = global_mean_pool(x, batch)
max_pool = global_max_pool(x, batch)
graph_repr = torch.cat([mean_pool, max_pool], dim=1)
if lab_feature.dim() == 1:
lab_embed = self.lab_embedding(lab_feature)
else:
lab_embed = self.lab_embedding(lab_feature.squeeze(-1))
combined = torch.cat([graph_repr, lab_embed], dim=1)
output = self.predictor(combined)
return output.squeeze()
# Backwards-compatible alias for previous naming
GINEModel = MoleculeMPNNModel
class HybridModel(nn.Module):
"""Hybrid model combining graph features with molecular descriptors."""
def __init__(
self,
*,
graph_model_class,
descriptor_dim: int,
graph_model_kwargs: Optional[Dict[str, Any]] = None,
graph_feature_dim: Optional[int] = None,
descriptor_hidden_dims: Optional[Sequence[int]] = None,
final_hidden_dims: Optional[Sequence[int]] = None,
dropout: float = 0.2,
use_batch_norm: bool = True,
output_dim: int = 1,
) -> None:
super().__init__()
graph_model_kwargs = dict(graph_model_kwargs or {})
if graph_feature_dim is None:
graph_feature_dim = graph_model_kwargs.get("hidden_dim")
if graph_feature_dim is None:
graph_feature_dim = 128
graph_model_kwargs.setdefault("hidden_dim", graph_feature_dim)
graph_model_kwargs.setdefault("output_dim", graph_feature_dim)
self.graph_model = graph_model_class(**graph_model_kwargs)
self.graph_feature_dim = graph_feature_dim
self.dropout = dropout
self.use_batch_norm = use_batch_norm
if descriptor_hidden_dims is None or len(descriptor_hidden_dims) == 0:
self.descriptor_net = nn.Identity()
self.descriptor_output_dim = descriptor_dim
else:
descriptor_layers: List[nn.Module] = []
in_dim = descriptor_dim
for hidden_dim in descriptor_hidden_dims:
descriptor_layers.append(nn.Linear(in_dim, hidden_dim))
if use_batch_norm:
descriptor_layers.append(nn.BatchNorm1d(hidden_dim))
descriptor_layers.append(nn.ReLU())
if dropout > 0:
descriptor_layers.append(nn.Dropout(dropout))
in_dim = hidden_dim
self.descriptor_net = nn.Sequential(*descriptor_layers)
self.descriptor_output_dim = in_dim
if final_hidden_dims is None or len(final_hidden_dims) == 0:
final_hidden_dims = [max(graph_feature_dim // 2, 1)]
final_layers: List[nn.Module] = []
in_dim = self.graph_feature_dim + self.descriptor_output_dim
for hidden_dim in final_hidden_dims:
final_layers.append(nn.Linear(in_dim, hidden_dim))
if use_batch_norm:
final_layers.append(nn.BatchNorm1d(hidden_dim))
final_layers.append(nn.ReLU())
if dropout > 0:
final_layers.append(nn.Dropout(dropout))
in_dim = hidden_dim
final_layers.append(nn.Linear(in_dim, output_dim))
self.final_predictor = nn.Sequential(*final_layers)
def forward(self, x, edge_index, batch, lab_feature, descriptors, edge_attr=None):
graph_features = self.graph_model(
x,
edge_index,
batch,
lab_feature,
edge_attr,
)
if graph_features.dim() == 1:
graph_features = graph_features.unsqueeze(0)
if descriptors.dim() == 1:
descriptors = descriptors.unsqueeze(0)
desc_features = self.descriptor_net(descriptors)
if isinstance(self.descriptor_net, nn.Identity):
desc_features = descriptors
combined = torch.cat([graph_features, desc_features], dim=1)
output = self.final_predictor(combined)
return output.squeeze(-1)
def create_model(model_type: str, input_dim: int, num_labs: int = 23, **kwargs):
"""Factory function to create models."""
models = {
'gcn': GraphConvModel,
'gin': GINEModel,
'gat': GATModel,
'mpnn': MoleculeMPNNModel,
}
if model_type not in models:
raise ValueError(f"Unknown model type: {model_type}")
return models[model_type](input_dim=input_dim, num_labs=num_labs, **kwargs)
class EarlyStopping:
"""Early stopping utility."""
def __init__(self, patience: int = 10, min_delta: float = 0.0, restore_best_weights: bool = True):
self.patience = patience
self.min_delta = min_delta
self.restore_best_weights = restore_best_weights
self.best_loss = None
self.counter = 0
self.best_weights = None
def __call__(self, val_loss: float, model: nn.Module) -> bool:
if self.best_loss is None:
self.best_loss = val_loss
self.save_checkpoint(model)
elif val_loss < self.best_loss - self.min_delta:
self.best_loss = val_loss
self.counter = 0
self.save_checkpoint(model)
else:
self.counter += 1
if self.counter >= self.patience:
if self.restore_best_weights and self.best_weights is not None:
model.load_state_dict(self.best_weights)
return True
return False
def save_checkpoint(self, model: nn.Module):
"""Save model weights."""
self.best_weights = model.state_dict().copy()
if __name__ == "__main__":
# Test model creation
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Create sample data
batch_size = 32
input_dim = 7 # Number of atom features
num_nodes = 20
num_edges = 40
x = torch.randn(num_nodes, input_dim)
edge_index = torch.randint(0, num_nodes, (2, num_edges))
batch = torch.zeros(num_nodes, dtype=torch.long)
lab_feature = torch.randint(0, 23, (1,))
# Test different models
models = ['gcn', 'gin', 'gat']
for model_type in models:
print(f"\nTesting {model_type.upper()} model:")
model = create_model(model_type, input_dim=input_dim, hidden_dim=64)
model.eval()
with torch.no_grad():
output = model(x, edge_index, batch, lab_feature)
print(f"Output shape: {output.shape}")
print(f"Output value: {output.item():.4f}") |