Datasets:
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import numpy as np
import importlib
from torch.nn import functional as F
from utils import ndcg_score
from model.ndcg_loss import LambdaNDCGLoss2
from model.additive_loss import PairwiseLogisticLoss
import pytorch_lightning as pl
def batch_ndcg_scores(labels, predictions, k):
labels_np = labels.detach().cpu().numpy()
predictions_np = predictions.detach().cpu().numpy()
scores = [ndcg_score(y_true, y_pred, k) for y_true, y_pred in zip(labels_np, predictions_np)]
return torch.tensor(scores, device=predictions.device, dtype=torch.float32)
#TODO: Implement seperately for binary and point-based models
class MInterface(pl.LightningModule):
def __init__(self, deg, loss, lr, **kargs):
super().__init__()
pl.seed_everything(kargs.get('seed'), workers=True)
self.save_hyperparameters()
self.deg = deg
self.load_model()
self.configure_loss()
self.ndcg_loss=LambdaNDCGLoss2(sigma=kargs.get('sigma_ndcg', 1))
self.logistic_loss = PairwiseLogisticLoss(sigma=kargs.get('sigma_logistic', 1))
def forward(self, ligand_data, protein_data, return_features=False):
if not return_features:
return self.model(ligand_data, protein_data)
return self.model(ligand_data, protein_data, return_features=return_features)
def training_step(self, batch, batch_idx):
ligand_data, protein_data, label = batch
# Get model prediction and bce loss
prediction = self(ligand_data, protein_data).view(-1, self.hparams.num_classes)
label = label.view(-1, self.hparams.num_classes)
loss = self.loss_function(prediction, label)
# Create query_lengths tensor
query_lengths = torch.full(
size=(prediction.shape[0],),
fill_value=self.hparams.num_classes,
dtype=torch.long,
device=prediction.device
)
# Add NDCG loss if enabled
if getattr(self.hparams, 'use_ndcg_loss', False):
ndcg_loss = compute_rank_loss(
loss_function=self.ndcg_loss,
predictions=prediction,
labels=label,
query_lengths=query_lengths
)
loss += ndcg_loss * getattr(self.hparams, 'ndcg_loss_weight', 1.0)
# Add logistic loss if enabled
if getattr(self.hparams, 'use_logistic_loss', False):
logistic_loss = compute_rank_loss(
loss_function=self.logistic_loss,
predictions=prediction,
labels=label,
query_lengths=query_lengths
)
loss += logistic_loss * getattr(self.hparams, 'logistic_loss_weight', 0.01)
# Log the total loss
self.log('loss', loss, on_step=True, on_epoch=True, prog_bar=True, batch_size=self.hparams.batch_size, sync_dist=False)
return loss
def validation_step(self, batch, batch_idx):
ligand_data, protein_data, label = batch
# Get model prediction and bce loss
prediction = self(ligand_data, protein_data).view(-1, self.hparams.num_classes)
label = label.view(-1, self.hparams.num_classes)
loss = self.loss_function(prediction, label)
# Create query_lengths tensor
query_lengths = torch.full(
size=(prediction.shape[0],),
fill_value=self.hparams.num_classes,
dtype=torch.long,
device=prediction.device
)
# Add NDCG loss if enabled
if getattr(self.hparams, 'use_ndcg_loss', False):
ndcg_loss = compute_rank_loss(
loss_function=self.ndcg_loss,
predictions=prediction,
labels=label,
query_lengths=query_lengths
)
loss += ndcg_loss * getattr(self.hparams, 'ndcg_loss_weight', 1.0)
# Add logistic loss if enabled
if getattr(self.hparams, 'use_logistic_loss', False):
logistic_loss = compute_rank_loss(
loss_function=self.logistic_loss,
predictions=prediction,
labels=label,
query_lengths=query_lengths
)
loss += logistic_loss * getattr(self.hparams, 'logistic_loss_weight', 0.01)
as_acc = batch_ndcg_scores(label, prediction, self.hparams.ndcg_k).mean()
self.log('val_loss', loss, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=self.hparams.batch_size)
self.log('val_as_acc', as_acc, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=self.hparams.batch_size)
if getattr(self.hparams, 'use_ndcg_loss', False):
self.log('val_ndcg_loss', ndcg_loss, on_step=False, on_epoch=True, prog_bar=False, sync_dist=True, batch_size=self.hparams.batch_size)
if getattr(self.hparams, 'use_logistic_loss', False):
self.log('val_logistic_loss', logistic_loss, on_step=False, on_epoch=True, prog_bar=False, sync_dist=True, batch_size=self.hparams.batch_size)
opt = self.optimizers() if hasattr(self, "optimizers") else None
lr_value = opt.param_groups[0]['lr'] if opt is not None else None
if lr_value is not None:
self.log('lr', lr_value, on_step=True, on_epoch=True, prog_bar=True, batch_size=self.hparams.batch_size, sync_dist=True)
return {
'val_loss': loss,
'val_as_acc': as_acc,
'lr': torch.tensor(lr_value, device=loss.device) if lr_value is not None else torch.tensor(float('nan'), device=loss.device)
}
def test_step(self, batch, batch_idx):
ligand_data, protein_data, label = batch
# Get model prediction and bce loss
prediction = self(ligand_data, protein_data).view(-1, self.hparams.num_classes)
label = label.view(-1, self.hparams.num_classes)
loss = self.loss_function(prediction, label)
# Create query_lengths tensor
query_lengths = torch.full(
size=(prediction.shape[0],),
fill_value=self.hparams.num_classes,
dtype=torch.long,
device=prediction.device
)
# Add NDCG loss if enabled
if getattr(self.hparams, 'use_ndcg_loss', False):
ndcg_loss = compute_rank_loss(
loss_function=self.ndcg_loss,
predictions=prediction,
labels=label,
query_lengths=query_lengths
)
loss += ndcg_loss * getattr(self.hparams, 'ndcg_loss_weight', 1.0)
# Add logistic loss if enabled
if getattr(self.hparams, 'use_logistic_loss', False):
logistic_loss = compute_rank_loss(
loss_function=self.logistic_loss,
predictions=prediction,
labels=label,
query_lengths=query_lengths
)
loss += logistic_loss * getattr(self.hparams, 'logistic_loss_weight', 0.01)
as_acc = batch_ndcg_scores(label, prediction, self.hparams.ndcg_k).mean()
self.log('test_loss', loss, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=self.hparams.batch_size)
self.log('test_as_acc', as_acc, on_step=False, on_epoch=True, prog_bar=True, sync_dist=True, batch_size=self.hparams.batch_size)
if getattr(self.hparams, 'use_ndcg_loss', False):
self.log('test_ndcg_loss', ndcg_loss, on_step=False, on_epoch=True, prog_bar=False, sync_dist=True, batch_size=self.hparams.batch_size)
if getattr(self.hparams, 'use_logistic_loss', False):
self.log('test_logistic_loss', logistic_loss, on_step=False, on_epoch=True, prog_bar=False, sync_dist=True, batch_size=self.hparams.batch_size)
names = protein_data.names if hasattr(protein_data, 'names') else None
names_array = np.array(names, dtype=object) if names is not None else None
output_dict = {
'test_loss': loss,
'test_as_acc': as_acc,
'out': prediction.detach().cpu().numpy(),
'labels': label.detach().cpu().numpy(),
'names': names_array,
}
if hasattr(self, "test_step_outputs"):
cpu_output_dict = {
'test_loss': float(loss.detach().cpu()),
'test_as_acc': float(as_acc.detach().cpu()),
'out': prediction.detach().cpu().numpy(),
'labels': label.detach().cpu().numpy(),
'names': names_array,
}
self.test_step_outputs.append(cpu_output_dict)
return output_dict
def on_test_epoch_end(self, outputs=None):
# Access the outputs through the dataloader_outputs attribute
if not hasattr(self, "test_step_outputs") or not self.test_step_outputs:
print("No outputs collected from test_step")
self.test_results = {"out": None, "labels": None}
return
# Process the collected outputs
all_out = np.concatenate([o["out"] for o in self.test_step_outputs])
all_labels = np.concatenate([o["labels"] for o in self.test_step_outputs])
all_names = None
if self.test_step_outputs[0]["names"] is not None:
all_names = np.concatenate([o["names"] for o in self.test_step_outputs])
results = {"out": all_out, "labels": all_labels, 'names': all_names}
if 'out_rmsd' in self.test_step_outputs[0]:
results['out_rmsd'] = np.concatenate([o['out_rmsd'] for o in self.test_step_outputs])
if 'labels_rmsd' in self.test_step_outputs[0]:
results['labels_rmsd'] = np.concatenate([o['labels_rmsd'] for o in self.test_step_outputs])
# Save for later use
self.test_results = results
# Clear the outputs to free memory
self.test_step_outputs = []
def on_test_epoch_start(self):
self.test_step_outputs = []
def on_validation_epoch_end(self):
# Make the Progress Bar leave there
self.print('')
def configure_optimizers(self):
# Configure a lr discount for the GNN layers to ensure stable training
gnn_params = []
other_params = []
for name, param in self.named_parameters():
if param.requires_grad:
# Check if parameter belongs to GNN layers
if any(gnn_layer in name for gnn_layer in ['gcn', 'gat', 'gin', 'sage', 'conv', 'gnn', 'egcl']):
gnn_params.append(param)
else:
other_params.append(param)
param_groups = []
if gnn_params:
param_groups.append({
'params': gnn_params,
'lr': self.hparams.lr * 0.5,
'name': 'gnn_layers'
})
if other_params:
param_groups.append({
'params': other_params,
'lr': self.hparams.lr * 1,
'name': 'other_layers'
})
# Adam optimizer, lr from hparams, no weight decay by default
optimizer = torch.optim.Adam(
param_groups
)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, mode='min', factor=0.95, patience=4, min_lr=1e-6
)
# ALTN: Custom scheduler with warm-up and cosine annealing
warmup_epochs = getattr(self.hparams, 'warmup_epochs', 10)
max_epochs = getattr(self.hparams, 'max_epochs', 100)
cycle_length = getattr(self.hparams, 'cycle_length', 20)
def combined_lr_lambda(epoch):
if epoch < warmup_epochs:
# Linear warm-up phase
return epoch / warmup_epochs
else:
# After warm-up: combine decay with cyclic behavior
progress = (epoch - warmup_epochs) / (max_epochs - warmup_epochs)
# Exponential decay component
decay_factor = 0.9 ** ((epoch - warmup_epochs) // 10)
# Cyclic component (triangular wave)
cycle_progress = ((epoch - warmup_epochs) % cycle_length) / cycle_length
cyclic_factor = 1.0 + 0.5 * np.sin(2 * np.pi * cycle_progress)
return decay_factor * cyclic_factor
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, combined_lr_lambda)
return {
"optimizer": optimizer,
"lr_scheduler": {
"scheduler": scheduler,
"monitor": "val_loss",
"interval": "epoch"
}
}
def configure_loss(self):
loss = self.hparams.loss.lower()
if loss == 'bce_with_logits':
self.loss_function = F.binary_cross_entropy_with_logits
elif loss == 'bce':
# self.loss_function = F.binary_cross_entropy
self.loss_function = F.binary_cross_entropy_with_logits
elif loss == 'ce':
self.loss_function = F.cross_entropy
elif loss == 'mse':
self.loss_function = F.mse_loss
else:
raise ValueError(f"Invalid loss type: {loss}")
def load_model(self):
name = 'molas'
cap_name = getattr(self.hparams, 'model', 'MolAS')
print(f'Name and Camel Name: {name}, {cap_name}')
try:
Model = getattr(importlib.import_module(
'.'+name, package=__package__), cap_name)
except:
raise ValueError(
f'Invalid Module File Name or Invalid Class Name {name}.{cap_name}!')
self.model = self.instancialize(Model)
def instancialize(self, Model, **other_args):
# Get model identifiers from hparams or defaults
num_node_features = getattr(self.hparams, 'num_node_features', 960)
num_classes = getattr(self.hparams, 'num_classes')
dropout_rate = getattr(self.hparams, 'dropout_rate', 0.3)
# Prepare arguments for the main model
args1 = {
'num_node_features': num_node_features,
'num_classes': num_classes,
'dropout_rate': dropout_rate
}
args1.update(other_args)
return Model(**args1)
def compute_rank_loss(loss_function, predictions, labels, query_lengths):
loss = loss_function(
scores=predictions,
relevance=labels,
n=query_lengths
)
# For the cases where batch size > 1
if loss.numel() > 1:
loss = loss.mean()
return loss
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