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3cc173b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 | """
Embedding Extractor for Mixture DCN GNN Model
Extracts the latent embeddings (before the MLP head) from your trained
mixture DCN predictor. These embeddings represent the molecule in the
learned latent space.
"""
import torch
import numpy as np
from typing import List, Optional
from torch_geometric.data import Data, Batch
class EmbeddingExtractor:
"""
Extract embeddings from mixture DCN GNN model.
The architecture is typically:
Graph Conv layers → Pooling → [EMBEDDING] → MLP → DCN prediction
^^^^^^^^^^
Extract this!
"""
def __init__(self, model, device='cpu'):
"""
Args:
model: Your trained mixture DCN model
device: 'cpu' or 'cuda'
"""
self.model = model
self.device = device
self.model.to(device)
self.model.eval()
# Hook to capture embeddings
self.embeddings = []
self._register_hook()
def _register_hook(self):
"""
Register a forward hook to capture embeddings.
You'll need to identify which layer is the embedding layer.
Common patterns:
- model.graph_conv_layers → model.pool → [model.embedding] → model.mlp
- model.encoder → [embedding] → model.decoder
Adjust based on your actual architecture!
"""
# OPTION 1: If your model has an explicit embedding layer
if hasattr(self.model, 'embedding_layer'):
self.hook = self.model.embedding_layer.register_forward_hook(
self._hook_fn
)
# OPTION 2: If MLP head is a separate module
elif hasattr(self.model, 'mlp_head') or hasattr(self.model, 'fc'):
# Hook the layer BEFORE the MLP head
# This is usually the pooling layer or the last graph conv
if hasattr(self.model, 'pool'):
self.hook = self.model.pool.register_forward_hook(
self._hook_fn
)
else:
# Find the last layer before MLP
layers = list(self.model.children())
self.hook = layers[-2].register_forward_hook(
self._hook_fn
)
# OPTION 3: Generic - hook the layer before final prediction
else:
# You may need to manually identify this
# Example: if your model is Sequential-like
layers = list(self.model.children())
# Hook second-to-last layer
self.hook = layers[-2].register_forward_hook(
self._hook_fn
)
def _hook_fn(self, module, input, output):
"""Capture the output of the hooked layer."""
# Detach and move to CPU to save memory
if isinstance(output, tuple):
# Some layers return (output, additional_info)
output = output[0]
self.embeddings.append(output.detach().cpu())
def extract_embeddings_from_graphs(self, graph_list: List[Data]) -> np.ndarray:
"""
Extract embeddings for a list of PyTorch Geometric graphs.
Args:
graph_list: List of PyG Data objects (molecule graphs)
Returns:
embeddings: numpy array of shape (n_molecules, embedding_dim)
"""
self.embeddings = []
with torch.no_grad():
# Batch the graphs
batch = Batch.from_data_list(graph_list).to(self.device)
# Forward pass (hook will capture embeddings)
_ = self.model(batch)
# Concatenate all captured embeddings
embeddings = torch.cat(self.embeddings, dim=0)
return embeddings.numpy()
def extract_embeddings_from_smiles(self,
smiles_list: List[str],
featurizer) -> np.ndarray:
"""
Extract embeddings from SMILES strings.
Args:
smiles_list: List of SMILES
featurizer: Function to convert SMILES → PyG Data object
Returns:
embeddings: numpy array of shape (n_molecules, embedding_dim)
"""
# Convert SMILES to graphs
graphs = [featurizer(smiles) for smiles in smiles_list]
# Extract embeddings
return self.extract_embeddings_from_graphs(graphs)
def __del__(self):
"""Remove hook when done."""
if hasattr(self, 'hook'):
self.hook.remove()
# =============================================================================
# USAGE EXAMPLE
# =============================================================================
"""
STEP 1: Identify your model architecture
-----------------------------------------
You need to know where the embedding layer is. Common patterns:
Pattern A: Explicit embedding
self.graph_conv = GCN(...)
self.pool = GlobalMeanPool()
self.embedding = Linear(hidden_dim, embedding_dim) ← Hook here!
self.mlp_head = MLP(embedding_dim, 1)
Pattern B: Pooling as embedding
self.graph_conv = GCN(...)
self.pool = GlobalMeanPool() ← Hook here! (output IS the embedding)
self.mlp_head = MLP(hidden_dim, 1)
Pattern C: Sequential
self.layers = Sequential(
GCN(...),
GlobalMeanPool(),
Linear(hidden_dim, embedding_dim), ← Hook here!
ReLU(),
Linear(embedding_dim, 1)
)
STEP 2: Load your model and extract embeddings
----------------------------------------------
from mixture_dcn_model import load_trained_model
# Load your trained model
model = load_trained_model('path/to/model.pth')
# Create extractor
extractor = EmbeddingExtractor(model, device='cuda')
# Extract embeddings for training set
train_embeddings = extractor.extract_embeddings_from_smiles(
train_smiles_list,
featurizer=your_featurizer_function
)
print(f"Embeddings shape: {train_embeddings.shape}")
# Output: (n_train_samples, embedding_dim)
STEP 3: Use these embeddings for One-Class SVM
----------------------------------------------
See applicability_domain.py for next steps!
"""
# =============================================================================
# DEBUGGING: Find the right layer to hook
# =============================================================================
def print_model_structure(model):
"""
Print model structure to help identify embedding layer.
Usage:
model = load_trained_model(...)
print_model_structure(model)
"""
print("Model Structure:")
print("="*70)
for i, (name, module) in enumerate(model.named_modules()):
if name: # Skip the root module
print(f"{i}: {name}")
print(f" Type: {type(module).__name__}")
# If it's a Linear layer, show dimensions
if hasattr(module, 'in_features') and hasattr(module, 'out_features'):
print(f" Shape: ({module.in_features}, {module.out_features})")
print()
"""
EXAMPLE OUTPUT:
Model Structure:
======================================================================
1: graph_conv
Type: GCN
2: pool
Type: GlobalMeanPool
3: embedding_layer ← THIS IS WHAT WE WANT!
Type: Linear
Shape: (256, 128) ← 128-dim embeddings
4: mlp_head
Type: Sequential
5: mlp_head.0
Type: Linear
Shape: (128, 64)
6: mlp_head.1
Type: ReLU
7: mlp_head.2
Type: Linear
Shape: (64, 1) ← Final prediction
""" |