import numpy as np
from tqdm import tqdm
from typing import Optional, Union, Dict, Any, Tuple, List, Literal
import warnings
import torch
from torch_geometric.loader import DataLoader
from torch_geometric.data import Data
from .model import GNN
from ..constant import GNN_ENCODER_MODELS, GNN_ENCODER_READOUTS, GNN_ENCODER_PARAMS
from ...base import BaseMolecularEncoder
from ...utils import graph_from_smiles
ALLOWABLE_ENCODER_MODELS = GNN_ENCODER_MODELS
ALLOWABLE_ENCODER_READOUTS = GNN_ENCODER_READOUTS
[docs]
class EdgePredMolecularEncoder(BaseMolecularEncoder):
"""This encoder implements a GNN-based model for molecular representation learning
using the edge prediction.
References
----------
- Paper: Strategies for Pre-training Graph Neural Networks (ICLR 2020) https://arxiv.org/abs/1905.12265
- Code: https://github.com/snap-stanford/pretrain-gnns/tree/master/chem
Parameters
----------
num_layer : int, default=5
Number of GNN layers.
hidden_size : int, default=300
Dimension of hidden node features.
drop_ratio : float, default=0.5
Dropout probability.
norm_layer : str, default="batch_norm"
Type of normalization layer to use. One of ["batch_norm", "layer_norm", "instance_norm", "graph_norm", "size_norm", "pair_norm"].
encoder_type : str, default="gin-virtual"
Type of GNN architecture to use.
readout : str, default="sum"
Method for aggregating node features to obtain graph-level representations.
batch_size : int, default=128
Number of samples per batch for training.
epochs : int, default=500
Maximum number of training epochs.
learning_rate : float, default=0.001
Learning rate for optimizer.
grad_clip_value : float, optional
Maximum norm of gradients for gradient clipping.
weight_decay : float, default=0.0
L2 regularization strength.
use_lr_scheduler : bool, default=False
Whether to use learning rate scheduler.
scheduler_factor : float, default=0.5
Factor by which to reduce learning rate when plateau is reached.
scheduler_patience : int, default=5
Number of epochs with no improvement after which learning rate will be reduced.
verbose : bool, default=False
Whether to print progress information during training.
device : Optional[Union[torch.device, str]], default=None
Device to run the model on (CPU or GPU).
model_name : str, default="EdgePredMolecularEncoder"
Name of the encoder model.
"""
def __init__(
self,
*,
num_layer: int = 5,
hidden_size: int = 300,
drop_ratio: float = 0.5,
norm_layer: str = "batch_norm",
encoder_type: str = "gin-virtual",
readout: str = "sum",
batch_size: int = 128,
epochs: int = 500,
learning_rate: float = 0.001,
grad_clip_value: Optional[float] = None,
weight_decay: float = 0.0,
use_lr_scheduler: bool = False,
scheduler_factor: float = 0.5,
scheduler_patience: int = 5,
verbose: bool = False,
device: Optional[Union[torch.device, str]] = None,
model_name: str = "EdgePredMolecularEncoder"
):
super().__init__(device=device, model_name=model_name)
self.num_layer = num_layer
self.hidden_size = hidden_size
self.drop_ratio = drop_ratio
self.norm_layer = norm_layer
self.encoder_type = encoder_type
self.readout = readout
self.batch_size = batch_size
self.epochs = epochs
self.learning_rate = learning_rate
self.grad_clip_value = grad_clip_value
self.weight_decay = weight_decay
self.use_lr_scheduler = use_lr_scheduler
self.scheduler_factor = scheduler_factor
self.scheduler_patience = scheduler_patience
self.verbose = verbose
self.fitting_loss = list()
self.fitting_epoch = 0
self.model_class = GNN
if self.encoder_type not in ALLOWABLE_ENCODER_MODELS:
raise ValueError(f"Invalid encoder_model: {self.encoder_type}. Currently only {ALLOWABLE_ENCODER_MODELS} are supported.")
if self.readout not in ALLOWABLE_ENCODER_READOUTS:
raise ValueError(f"Invalid encoder_readout: {self.readout}. Currently only {ALLOWABLE_ENCODER_READOUTS} are supported.")
@staticmethod
def _get_param_names() -> List[str]:
return GNN_ENCODER_PARAMS.copy()
def _get_model_params(self, checkpoint: Optional[Dict] = None) -> Dict[str, Any]:
params = {
"num_layer": self.num_layer,
"hidden_size": self.hidden_size,
"encoder_type": self.encoder_type,
"drop_ratio": self.drop_ratio,
"norm_layer": self.norm_layer,
"readout": self.readout,
}
if checkpoint is not None:
if "hyperparameters" not in checkpoint:
raise ValueError("Checkpoint missing 'hyperparameters' key")
params = {k: checkpoint["hyperparameters"].get(k, v) for k, v in params.items()}
return params
def _convert_to_pytorch_data(self, X):
"""Convert numpy arrays to PyTorch Geometric data format.
"""
if self.verbose:
iterator = tqdm(enumerate(X), desc="Converting molecules to graphs", total=len(X))
else:
iterator = enumerate(X)
pyg_graph_list = []
for idx, smiles_or_mol in iterator:
graph = graph_from_smiles(smiles_or_mol, None)
g = Data()
g.num_nodes = graph["num_nodes"]
g.edge_index = torch.from_numpy(graph["edge_index"])
del graph["num_nodes"]
del graph["edge_index"]
if graph["edge_feat"] is not None:
g.edge_attr = torch.from_numpy(graph["edge_feat"])
del graph["edge_feat"]
if graph["node_feat"] is not None:
g.x = torch.from_numpy(graph["node_feat"])
del graph["node_feat"]
pyg_graph_list.append(g)
return pyg_graph_list
def _setup_optimizers(self) -> Tuple[torch.optim.Optimizer, Optional[Any]]:
"""Setup optimization components including optimizer and learning rate scheduler.
"""
optimizer = torch.optim.Adam(self.model.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
scheduler = None
if self.use_lr_scheduler:
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode="min",
factor=self.scheduler_factor,
patience=self.scheduler_patience,
min_lr=1e-6,
cooldown=0,
eps=1e-8,
)
return optimizer, scheduler
[docs]
def fit(
self,
X_train: List[str],
) -> "EdgePredMolecularEncoder":
"""Fit the model to the training data with optional validation set.
Parameters
----------
X_train : List[str]
Training set input molecular structures as SMILES strings
Returns
-------
self : EdgePredMolecularEncoder
Fitted estimator
"""
self._initialize_model(self.model_class)
self.model.initialize_parameters()
optimizer, scheduler = self._setup_optimizers()
# Prepare datasets and loaders
X_train, _ = self._validate_inputs(X_train, return_rdkit_mol=True)
train_dataset = self._convert_to_pytorch_data(X_train)
train_loader = DataLoader(
train_dataset,
batch_size=self.batch_size,
shuffle=True,
num_workers=0
)
self.fitting_loss = []
# Calculate total steps for progress tracking
total_steps = self.epochs * len(train_loader)
# Initialize global progress bar
global_pbar = tqdm(total=total_steps, desc="Training Progress", disable=not self.verbose)
for epoch in range(self.epochs):
# Training phase
train_losses = self._train_epoch(train_loader, optimizer, epoch, global_pbar)
self.fitting_loss.append(np.mean(train_losses))
if scheduler:
scheduler.step(np.mean(train_losses))
global_pbar.close()
self.fitting_epoch = epoch
self.is_fitted_ = True
return self
def _train_epoch(self, train_loader, optimizer, epoch, global_pbar=None):
"""Training logic for one epoch.
Args:
train_loader: DataLoader containing training data
optimizer: Optimizer instance for model parameter updates
epoch: Current epoch number
global_pbar: Global progress bar for tracking overall progress
Returns:
list: List of loss values for each training step
"""
self.model.train()
losses = []
for step, batch in enumerate(train_loader):
batch = batch.to(self.device)
optimizer.zero_grad()
loss = self.model.compute_loss(batch)
loss.backward()
if self.grad_clip_value is not None:
torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.grad_clip_value)
optimizer.step()
losses.append(loss.item())
# Update global progress bar
if global_pbar is not None:
global_pbar.set_postfix({
"Epoch": f"{epoch+1}/{self.epochs}",
"Step": f"{step+1}/{len(train_loader)}",
"Loss": f"{loss.item():.4f}"
})
global_pbar.update(1)
return losses
[docs]
def encode(self, X: List[str], return_type: Literal["np", "pt"] = "pt") -> Union[np.ndarray, torch.Tensor]:
"""Encode molecules into vector representations.
Parameters
----------
X : List[str]
List of SMILES strings
return_type : Literal["np", "pt"], default="pt"
Return type of the representations
Returns
-------
representations : ndarray or torch.Tensor
Molecular representations
"""
self._check_is_fitted()
# Convert to PyTorch Geometric format and create loader
X, _ = self._validate_inputs(X, return_rdkit_mol=True)
dataset = self._convert_to_pytorch_data(X)
loader = DataLoader(dataset, batch_size=self.batch_size, shuffle=False)
if self.model is None:
raise RuntimeError("Model not initialized")
# Generate encodings
self.model = self.model.to(self.device)
self.model.eval()
encodings = []
with torch.no_grad():
for batch in tqdm(loader, disable=not self.verbose):
batch = batch.to(self.device)
out = self.model(batch)
encodings.append(out["graph"].cpu())
# Concatenate and convert to requested format
encodings = torch.cat(encodings, dim=0)
return encodings if return_type == "pt" else encodings.numpy()