def _load_lba_data(datafiles, dist, maxnum):
"""
Load LBA datasets from LMDB format.
:param datafiles: Dictionary of LMDB dataset directories.
:type datafiles: dict
:param radius: Radius of the selected region around the ligand.
:type radius: float
:param maxnum: Maximum total number of atoms of the ligand and the region around it.
:type radius: int
:return datasets: Dictionary of processed dataset objects.
:rtype datasets: dict
"""
datasets = {}
for split, datafile in datafiles.items():
dataset = LMDBDataset(datafile, transform=TransformLBA(dist, maxnum, move_lig=False))
# Load original atoms
dsdict = extract_coordinates_as_numpy_arrays(dataset, atom_frames=['atoms_pocket','atoms_ligand'])
# Add the label data
dsdict['neglog_aff'] = np.array([item['scores']['neglog_aff'] for item in dataset])
# Convert everything to tensors
datasets[split] = {key: torch.from_numpy(val) for key, val in dsdict.items()}
return datasets
def _load_lba_data_siamese(datafiles, dist, maxnum):
"""
Load LBA datasets from LMDB format.
:param datafiles: Dictionary of LMDB dataset directories.
:type datafiles: dict
:param radius: Radius of the selected region around the mutated residue.
:type radius: float
:param maxnum: Maximum total number of atoms of the ligand and the region around it.
:type radius: int
:return datasets: Dictionary of processed dataset objects.
:rtype datasets: dict
"""
datasets = {}
key_names = ['index', 'num_atoms', 'charges', 'positions']
for split, datafile in datafiles.items():
dataset = LMDBDataset(datafile, transform=TransformLBA(dist, maxnum, move_lig=True))
# Load original atoms
bound = extract_coordinates_as_numpy_arrays(dataset, atom_frames=['atoms_pocket','atoms_ligand'])
for k in key_names: bound['bound_'+k] = bound.pop(k)
# Load mutated atoms
apart = extract_coordinates_as_numpy_arrays(dataset, atom_frames=['atoms_pocket','atoms_ligand_moved'])
for k in key_names: apart['apart_'+k] = apart.pop(k)
# Merge datasets with atoms
dsdict = {**bound, **apart}
# Add the label data
dsdict['neglog_aff'] = np.array([item['scores']['neglog_aff'] for item in dataset])
# Convert everything to tensors
datasets[split] = {key: torch.from_numpy(val) for key, val in dsdict.items()}
return datasets
def _load_msp_data(datafiles, radius, droph=False):
"""
Load MSP datasets from LMDB format.
:param datafiles: Dictionary of LMDB dataset directories.
:type datafiles: dict
:param radius: Radius of the selected region around the mutated residue.
:type radius: float
:param radius: Drop hydrogen atoms.
:type radius: bool
:return datasets: Dictionary of processed dataset objects.
:rtype datasets: dict
"""
datasets = {}
key_names = ['index', 'num_atoms', 'charges', 'positions']
for split, datafile in datafiles.items():
dataset = LMDBDataset(datafile, transform=EnvironmentSelection(radius, droph))
# Load original atoms
ori = extract_coordinates_as_numpy_arrays(dataset, atom_frames=['original_atoms'])
for k in key_names: ori['original_'+k] = ori.pop(k)
# Load mutated atoms
mut = extract_coordinates_as_numpy_arrays(dataset, atom_frames=['mutated_atoms'])
for k in key_names: mut['mutated_'+k] = mut.pop(k)
# Merge datasets with atoms
dsdict = {**ori, **mut}
# Add labels
labels = [dataset[i]['label'] for i in range(len(dataset))]
dsdict['label'] = np.array(labels, dtype=int)
# Convert everything to tensors
datasets[split] = {key: torch.from_numpy(val) for key, val in dsdict.items()}
return datasets
def __init__(self, lmdb_path, testing, random_seed=None, **kwargs):
self._lmdb_dataset = LMDBDataset(lmdb_path)
self.testing = testing
self.random_seed = random_seed
self.grid_config = dotdict({
# Mapping from elements to position in channel dimension.
'element_mapping': {
'C': 0,
'O': 1,
'N': 2,
'S': 3
},
# Radius of the grids to generate, in angstroms.
'radius': 17.0,
# Resolution of each voxel, in angstroms.
'resolution': 1.0,
# Number of directions to apply for data augmentation.
'num_directions': 20,
# Number of rolls to apply for data augmentation.
'num_rolls': 20,
### PPI specific
# Number of negatives to sample per positive example. -1 means all.
'neg_to_pos_ratio': 1,
'neg_to_pos_ratio_testing': 1,
# Max number of positive regions to take from a structure. -1 means all.
'max_pos_regions_per_ensemble': 5,
'max_pos_regions_per_ensemble_testing': 5,
# Whether to use all negative at test time.
'full_test': False,
})
# Update grid configs as necessary
self.grid_config.update(kwargs)
def _load_smp_data(datafiles):
"""
Load SMP datasets from LMDB format.
:param datafiles: Dictionary of LMDB dataset directories.
:type datafiles: dict
:param radius: Radius of the selected region around the ligand.
:type radius: float
:param maxnum: Maximum total number of atoms of the ligand and the region around it.
:type radius: int
:return datasets: Dictionary of processed dataset objects.
:rtype datasets: dict
"""
datasets = {}
for split, datafile in datafiles.items():
dataset = LMDBDataset(datafile)
# Load original atoms
dsdict = extract_coordinates_as_numpy_arrays(dataset, atom_frames=['atoms'])
# Add the label data
labels = np.zeros([len(label_names),len(dataset)])
for i, item in enumerate(dataset):
labels[:,i] = item['labels']
for j, label in enumerate(label_names):
dsdict[label] = labels[j]
# Convert everything to tensors
datasets[split] = {key: torch.from_numpy(val) for key, val in dsdict.items()}
return datasets
def _load_res_data(datafiles,
maxnum,
samples=42,
keep=['C', 'N', 'O', 'P', 'S'],
seed=1):
"""
Load RES datasets from LMDB format.
:param datafiles: Dictionary of LMDB dataset directories.
:type datafiles: dict
:param radius: Maximum number of atoms to consider.
:type radius: int
:param samples: Number of sample structures.
:type samples: int
:return datasets: Dictionary of processed dataset objects.
:rtype datasets: dict
"""
datasets = {}
key_names = ['index', 'num_atoms', 'charges', 'positions']
for split, datafile in datafiles.items():
dataset = LMDBDataset(datafile)
# Randomly pick the samples.
numpy.random.seed(seed)
indices = np.random.choice(np.arange(len(dataset)),
size=int(samples),
replace=True)
# Get labels
labels = np.concatenate(
[dataset[i]['labels']['label'] for i in indices])
print('Labels:', labels)
# Load original atoms
dsdict = _extract_coordinates_as_numpy_arrays(dataset,
indices=indices,
keep=keep)
for k in key_names:
dsdict[k] = dsdict.pop(k)
# Add labels
dsdict['label'] = np.array(labels, dtype=int)
print('Sizes:')
for key, val in dsdict.items():
print(key, len(val))
# Convert everything to tensors
datasets[split] = {
key: torch.from_numpy(val)
for key, val in dsdict.items()
}
return datasets
def _load_lep_data(datafiles, radius, droph, maxnum):
"""
Load LEP datasets from LMDB format.
:param datafiles: Dictionary of LMDB dataset directories.
:type datafiles: dict
:param radius: Radius of the selected region around the mutated residue.
:type radius: float
:param radius: Drop hydrogen atoms.
:type radius: bool
:param radius: Maximum number of atoms to consider.
:type radius: int
:return datasets: Dictionary of processed dataset objects.
:rtype datasets: dict
"""
datasets = {}
key_names = ['index', 'num_atoms', 'charges', 'positions']
for split, datafile in datafiles.items():
dataset = LMDBDataset(datafile, transform=EnvironmentSelection(radius, droph, maxnum))
# Load original atoms
act = extract_coordinates_as_numpy_arrays(dataset, atom_frames=['atoms_active'])
for k in key_names: act[k+'_active'] = act.pop(k)
# Load mutated atoms
ina = extract_coordinates_as_numpy_arrays(dataset, atom_frames=['atoms_inactive'])
for k in key_names: ina[k+'_inactive'] = ina.pop(k)
# Merge datasets with atoms
dsdict = {**act, **ina}
# Add labels (1 for active, 0 for inactive)
ldict = {'A':1, 'I':0}
labels = [ldict[dataset[i]['label']] for i in range(len(dataset))]
dsdict['label'] = np.array(labels, dtype=int)
# Convert everything to tensors
datasets[split] = {key: torch.from_numpy(val) for key, val in dsdict.items()}
return datasets
def train(args, device, log_dir, rep=None, test_mode=False):
# logger = logging.getLogger('lba')
# logger.basicConfig(filename=os.path.join(log_dir, f'train_{split}_cv{fold}.log'),level=logging.INFO)
train_dataset = LMDBDataset(os.path.join(args.data_dir, 'train'),
transform=GNNTransformSMP(args.target_name))
val_dataset = LMDBDataset(os.path.join(args.data_dir, 'val'),
transform=GNNTransformSMP(args.target_name))
test_dataset = LMDBDataset(os.path.join(args.data_dir, 'test'),
transform=GNNTransformSMP(args.target_name))
train_loader = DataLoader(train_dataset,
args.batch_size,
shuffle=True,
num_workers=4)
val_loader = DataLoader(val_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
test_loader = DataLoader(test_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
for data in train_loader:
num_features = data.num_features
break
model = GNN_SMP(num_features, dim=args.hidden_dim).to(device)
model.to(device)
best_val_loss = 999
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.7,
patience=3,
min_lr=0.00001)
for epoch in range(1, args.num_epochs + 1):
start = time.time()
train_loss = train_loop(model, train_loader, optimizer, device)
print('validating...')
val_loss, _, _ = test(model, val_loader, device)
scheduler.step(val_loss)
if val_loss < best_val_loss:
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': train_loss,
}, os.path.join(log_dir, f'best_weights_rep{rep}.pt'))
best_val_loss = val_loss
elapsed = (time.time() - start)
print('Epoch: {:03d}, Time: {:.3f} s'.format(epoch, elapsed))
print('\tTrain Loss: {:.7f}, Val MAE: {:.7f}'.format(
train_loss, val_loss))
if test_mode:
train_file = os.path.join(log_dir, f'smp-rep{rep}.best.train.pt')
val_file = os.path.join(log_dir, f'smp-rep{rep}.best.val.pt')
test_file = os.path.join(log_dir, f'smp-rep{rep}.best.test.pt')
cpt = torch.load(os.path.join(log_dir, f'best_weights_rep{rep}.pt'))
model.load_state_dict(cpt['model_state_dict'])
_, y_true_train, y_pred_train = test(model, train_loader, device)
torch.save({
'targets': y_true_train,
'predictions': y_pred_train
}, train_file)
_, y_true_val, y_pred_val = test(model, val_loader, device)
torch.save({
'targets': y_true_val,
'predictions': y_pred_val
}, val_file)
mae, y_true_test, y_pred_test = test(model, test_loader, device)
print(f'\tTest MAE {mae}')
torch.save({
'targets': y_true_test,
'predictions': y_pred_test
}, test_file)
def train(args, device, test_mode=False):
print("Training model with config:")
print(str(json.dumps(args.__dict__, indent=4)) + "\n")
# Save config
with open(os.path.join(args.output_dir, 'config.json'), 'w') as f:
json.dump(args.__dict__, f, indent=4)
np.random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
train_dataset = LMDBDataset(
os.path.join(args.data_dir, 'train'),
transform=CNN3D_TransformRSR(random_seed=args.random_seed))
val_dataset = LMDBDataset(
os.path.join(args.data_dir, 'val'),
transform=CNN3D_TransformRSR(random_seed=args.random_seed))
test_dataset = LMDBDataset(
os.path.join(args.data_dir, 'test'),
transform=CNN3D_TransformRSR(random_seed=args.random_seed))
train_loader = DataLoader(train_dataset, args.batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, args.batch_size, shuffle=False)
test_loader = DataLoader(test_dataset, args.batch_size, shuffle=False)
for data in train_loader:
in_channels, spatial_size = data['feature'].size()[1:3]
print('num channels: {:}, spatial size: {:}'.format(
in_channels, spatial_size))
break
model = conv_model(in_channels, spatial_size, args)
print(model)
model.to(device)
best_val_loss = np.Inf
best_corrs = None
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
for epoch in range(1, args.num_epochs + 1):
start = time.time()
train_loss = train_loop(model, train_loader, optimizer, device)
val_loss, corrs, val_df = test(model, val_loader, device)
if val_loss < best_val_loss:
print(
f"\nSave model at epoch {epoch:03d}, val_loss: {val_loss:.4f}")
save_weights(model,
os.path.join(args.output_dir, f'best_weights.pt'))
best_val_loss = val_loss
best_corrs = corrs
elapsed = (time.time() - start)
print('Epoch {:03d} finished in : {:.3f} s'.format(epoch, elapsed))
print(
'\tTrain RMSE: {:.7f}, Val RMSE: {:.7f}, Per-target Spearman R: {:.7f}, Global Spearman R: {:.7f}'
.format(train_loss, val_loss, corrs['per_target_spearman'],
corrs['all_spearman']))
if test_mode:
model.load_state_dict(
torch.load(os.path.join(args.output_dir, f'best_weights.pt')))
rmse, corrs, test_df = test(model, test_loader, device)
test_df.to_pickle(os.path.join(args.output_dir, 'test_results.pkl'))
print(
'Test RMSE: {:.7f}, Per-target Spearman R: {:.7f}, Global Spearman R: {:.7f}'
.format(rmse, corrs['per_target_spearman'], corrs['all_spearman']))
test_file = os.path.join(args.output_dir, f'test_results.txt')
with open(test_file, 'a+') as out:
out.write('{}\t{:.7f}\t{:.7f}\t{:.7f}\n'.format(
args.random_seed, rmse, corrs['per_target_spearman'],
corrs['all_spearman']))
return best_val_loss, best_corrs['per_target_spearman'], best_corrs[
'all_spearman']
graph.mut_idx[total_n_mut:total_n_mut +
graph.num_mut_atoms[i + 1]] += total_n
return graph
def __call__(self, batch):
return self.collate(batch)
if __name__ == "__main__":
from tqdm import tqdm
# dataset = LMDBDataset(os.path.join('/scratch/users/raphtown/atom3d_mirror/lmdb/MSP/splits/split-by-sequence-identity-30/data', 'train'))
# dataloader = DataLoader(dataset, batch_size=3, shuffle=False, num_workers=4)
# for i, item in tqdm(enumerate(dataloader)):
# if i < 578:
# continue
# print(item)
dataset = LMDBDataset(os.path.join(
'/scratch/users/raphtown/atom3d_mirror/lmdb/MSP/splits/split-by-sequence-identity-30/data',
'train'),
transform=GNNTransformMSP())
dataloader = DataLoader(dataset,
batch_size=3,
shuffle=False,
collate_fn=CollaterMSP(batch_size=3),
num_workers=4)
for original, mutated in tqdm(dataloader):
if mutated.mut_idx.max() > mutated.batch.shape[0]:
print(mutated.batch.shape)
print(mutated.mut_idx)
break
# Assume labels/classes are integers (0, 1, 2, ...).
labels = [item['label'] for item in dataset]
classes, class_sample_count = np.unique(labels, return_counts=True)
# Weighted sampler for imbalanced classification (1:1 ratio for each class)
weight = 1. / class_sample_count
sample_weights = torch.tensor([weight[t] for t in labels])
sampler = torch.utils.data.WeightedRandomSampler(weights=sample_weights,
num_samples=len(dataset),
replacement=True)
return sampler
if __name__ == "__main__":
dataset_path = os.path.join(os.environ['MSP_DATA'], 'val')
dataset = LMDBDataset(dataset_path,
transform=CNN3D_TransformMSP(add_flag=True,
center_at_mut=True,
radius=10.0))
dataloader = DataLoader(dataset,
batch_size=8,
sampler=create_balanced_sampler(dataset))
non_zeros = []
for item in dataloader:
print('feature original shape:', item['feature_original'].shape)
print('feature mutated shape:', item['feature_mutated'].shape)
print('label:', item['label'])
print('id:', item['id'])
non_zeros.append(np.count_nonzero(item['label']))
break
for item in dataloader:
non_zeros.append(np.count_nonzero(item['label']))
item = prot_graph_transform(item, ['atoms'], 'scores')
graph = item['atoms']
graph.y = torch.FloatTensor([graph.y['rms']])
split = item['id'].split("'")
graph.target = split[1]
graph.decoy = split[3]
return graph
if __name__ == "__main__":
save_dir = '/scratch/users/aderry/atom3d/rsr'
data_dir = '/scratch/users/raphtown/atom3d_mirror/lmdb/RSR/splits/candidates-split-by-time/data'
os.makedirs(os.path.join(save_dir, 'train'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'val'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'test'), exist_ok=True)
train_dataset = LMDBDataset(os.path.join(data_dir, 'train'),
transform=GNNTransformRSR())
val_dataset = LMDBDataset(os.path.join(data_dir, 'val'),
transform=GNNTransformRSR())
test_dataset = LMDBDataset(os.path.join(data_dir, 'test'),
transform=GNNTransformRSR())
# train_loader = DataLoader(train_dataset, 1, shuffle=True, num_workers=4)
# val_loader = DataLoader(val_dataset, 1, shuffle=False, num_workers=4)
# test_loader = DataLoader(test_dataset, 1, shuffle=False, num_workers=4)
# for item in dataset[0]:
# print(item, type(dataset[0][item]))
print('processing train dataset...')
for i, item in enumerate(tqdm(train_dataset)):
torch.save(item, os.path.join(save_dir, 'train', f'data_{i}.pt'))
print('processing validation dataset...')
import numpy as np
import os
from atom3d.util.transforms import prot_graph_transform, PairedGraphTransform
from atom3d.datasets import LMDBDataset
from torch_geometric.data import Data, Dataset, DataLoader
import atom3d.util.graph as gr
class GNNTransformLEP(object):
def __init__(self, atom_keys, label_key):
self.atom_keys = atom_keys
self.label_key = label_key
def __call__(self, item):
# transform protein and/or pocket to PTG graphs
item = prot_graph_transform(item, atom_keys=self.atom_keys, label_key=self.label_key)
return item
if __name__=="__main__":
dataset = LMDBDataset(os.path.join('/scratch/users/raphtown/atom3d_mirror/lmdb/LEP/splits/split-by-protein/data', 'train'), transform=PairedGraphTransform('atoms_active', 'atoms_inactive', label_key='label'))
dataloader = DataLoader(dataset, batch_size=4, shuffle=False)
for active, inactive in dataloader:
print(active)
print(inactive)
break
# for item in dataloader:
# print(item)
# break
# Assume labels/classes are integers (0, 1, 2, ...).
labels = [item['label'] for item in dataset]
classes, class_sample_count = np.unique(labels, return_counts=True)
# Weighted sampler for imbalanced classification (1:1 ratio for each class)
weight = 1. / class_sample_count
sample_weights = torch.tensor([weight[t] for t in labels])
sampler = torch.utils.data.WeightedRandomSampler(weights=sample_weights,
num_samples=len(dataset),
replacement=True)
return sampler
if __name__ == "__main__":
dataset_path = os.path.join(os.environ['LEP_DATA'], 'val')
dataset = LMDBDataset(dataset_path,
transform=CNN3D_TransformLEP(add_flag=True,
radius=10.0))
dataloader = DataLoader(dataset,
batch_size=8,
sampler=create_balanced_sampler(dataset))
non_zeros = []
for item in dataloader:
print('feature inactive shape:', item['feature_inactive'].shape)
print('feature active shape:', item['feature_active'].shape)
print('label:', item['label'])
print('id:', item['id'])
non_zeros.append(np.count_nonzero(item['label']))
break
for item in dataloader:
non_zeros.append(np.count_nonzero(item['label']))
import numpy as np
import os
import torch
from atom3d.util.transforms import prot_graph_transform
from atom3d.datasets import LMDBDataset
from torch_geometric.data import Data, Dataset, DataLoader
class GNNTransformPSR(object):
def __init__(self):
pass
def __call__(self, item):
item = prot_graph_transform(item, ['atoms'], 'scores')
graph = item['atoms']
graph.y = torch.FloatTensor([graph.y['gdt_ts']])
graph.target = item['id'][0]
graph.decoy = item['id'][1]
return graph
if __name__=="__main__":
dataset = LMDBDataset('/scratch/users/raphtown/atom3d_mirror/lmdb/PSR/splits/split-by-year/data/train', transform=GNNTransformPSR())
dataloader = DataLoader(dataset, batch_size=4, shuffle=False)
# for item in dataset[0]:
# print(item, type(dataset[0][item]))
for item in dataloader:
print(item)
break
import os
import torch
from atom3d.util.transforms import prot_graph_transform
from atom3d.datasets import LMDBDataset
from torch_geometric.data import Data, Dataset, DataLoader
class GNNTransformRSR(object):
def __init__(self):
pass
def __call__(self, item):
item = prot_graph_transform(item, ['atoms'], 'scores')
graph = item['atoms']
graph.y = torch.FloatTensor([graph.y['rms']])
graph.target = item['id'][0]
graph.decoy = item['id'][1]
return graph
if __name__ == "__main__":
dataset = LMDBDataset(
'/scratch/users/raphtown/atom3d_mirror/lmdb/RSR/splits/candidates-split-by-time/data/train',
transform=GNNTransformRSR())
dataloader = DataLoader(dataset, batch_size=4, shuffle=False)
# for item in dataset[0]:
# print(item, type(dataset[0][item]))
for item in dataloader:
print(item)
break
def train(args, device, test_mode=False):
print("Training model with config:")
print(str(json.dumps(args.__dict__, indent=4)) + "\n")
# Save config
with open(os.path.join(args.output_dir, 'config.json'), 'w') as f:
json.dump(args.__dict__, f, indent=4)
np.random.seed(args.random_seed)
torch.manual_seed(args.random_seed)
train_dataset = LMDBDataset(
os.path.join(args.data_dir, 'train'),
transform=CNN3D_TransformMSP(args.add_flag, args.center_at_mut, random_seed=args.random_seed))
val_dataset = LMDBDataset(
os.path.join(args.data_dir, 'val'),
transform=CNN3D_TransformMSP(args.add_flag, args.center_at_mut, random_seed=args.random_seed))
test_dataset = LMDBDataset(
os.path.join(args.data_dir, 'test'),
transform=CNN3D_TransformMSP(args.add_flag, args.center_at_mut, random_seed=args.random_seed))
train_loader = DataLoader(train_dataset, args.batch_size,
sampler=create_balanced_sampler(train_dataset))
val_loader = DataLoader(val_dataset, args.batch_size,
sampler=create_balanced_sampler(val_dataset))
test_loader = DataLoader(test_dataset, args.batch_size, shuffle=False)
for data in train_loader:
in_channels, spatial_size = data['feature_original'].size()[1:3]
print('num channels: {:}, spatial size: {:}'.format(in_channels, spatial_size))
break
model = conv_model(in_channels, spatial_size, args)
print(model)
model.to(device)
prev_val_loss = np.Inf
best_val_loss = np.Inf
best_val_auroc = 0
best_stats = None
criterion = nn.BCELoss()
criterion.to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
for epoch in range(1, args.num_epochs+1):
start = time.time()
train_loss = train_loop(model, train_loader, args.repeat_gen, criterion,
optimizer, device)
val_loss, stats, val_df = test(model, val_loader, args.repeat_gen,
criterion, device)
elapsed = (time.time() - start)
print(f'Epoch {epoch:03d} finished in : {elapsed:.3f} s')
print(f"\tTrain loss {train_loss:.4f}, Val loss: {val_loss:.4f}, "
f"Val AUROC: {stats['auroc']:.4f}, Val AUPRC: {stats['auroc']:.4f}")
#if stats['auroc'] > best_val_auroc:
if val_loss < best_val_loss:
print(f"\nSave model at epoch {epoch:03d}, val_loss: {val_loss:.4f}, "
f"auroc: {stats['auroc']:.4f}, auprc: {stats['auroc']:.4f}")
save_weights(model, os.path.join(args.output_dir, f'best_weights.pt'))
best_val_loss = val_loss
best_val_auroc = stats['auroc']
best_stats = stats
if args.early_stopping and val_loss >= prev_val_loss:
print(f"Validation loss stopped decreasing, stopping at epoch {epoch:03d}...")
break
prev_val_loss = val_loss
if test_mode:
model.load_state_dict(torch.load(os.path.join(args.output_dir, f'best_weights.pt')))
test_loss, stats, test_df = test(model, test_loader, args.repeat_gen,
criterion, device)
test_df.to_pickle(os.path.join(args.output_dir, 'test_results.pkl'))
print(f"Test loss: {test_loss:.4f}, Test AUROC: {stats['auroc']:.4f}, "
f"Test AUPRC: {stats['auprc']:.4f}")
test_file = os.path.join(args.output_dir, f'test_results.txt')
with open(test_file, 'w') as f:
f.write(f"test_loss\tAUROC\tAUPRC\n")
f.write(f"{test_loss:}\t{stats['auroc']:}\t{stats['auprc']:}\n")
pass
def __call__(self, data_list):
batch_1 = Batch.from_data_list([d[0] for d in data_list])
batch_2 = Batch.from_data_list([d[1] for d in data_list])
return batch_1, batch_2
if __name__=="__main__":
save_dir = '/scratch/users/aderry/atom3d/lep'
data_dir = '/scratch/users/raphtown/atom3d_mirror/lmdb/LEP/splits/split-by-protein/data'
os.makedirs(os.path.join(save_dir, 'train'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'val'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'test'), exist_ok=True)
transform = PairedGraphTransform('atoms_active', 'atoms_inactive', label_key='label')
train_dataset = LMDBDataset(os.path.join(data_dir, 'train'), transform=transform)
val_dataset = LMDBDataset(os.path.join(data_dir, 'val'), transform=transform)
test_dataset = LMDBDataset(os.path.join(data_dir, 'test'), transform=transform)
# train_loader = DataLoader(train_dataset, 1, shuffle=True, num_workers=4)
# val_loader = DataLoader(val_dataset, 1, shuffle=False, num_workers=4)
# test_loader = DataLoader(test_dataset, 1, shuffle=False, num_workers=4)
# for item in dataset[0]:
# print(item, type(dataset[0][item]))
for i, item in enumerate(tqdm(train_dataset)):
torch.save(item, os.path.join(save_dir, 'train', f'data_{i}.pt'))
for i, item in enumerate(tqdm(val_dataset)):
torch.save(item, os.path.join(save_dir, 'val', f'data_{i}.pt'))
for i, item in enumerate(tqdm(test_dataset)):
def train(args, device, log_dir, rep=None, test_mode=False):
# logger = logging.getLogger('lba')
# logger.basicConfig(filename=os.path.join(log_dir, f'train_{split}_cv{fold}.log'),level=logging.INFO)
train_dataset = LMDBDataset(os.path.join(args.data_dir, 'train'),
transform=GNNTransformRSR())
val_dataset = LMDBDataset(os.path.join(args.data_dir, 'val'),
transform=GNNTransformRSR())
test_dataset = LMDBDataset(os.path.join(args.data_dir, 'test'),
transform=GNNTransformRSR())
train_loader = DataLoader(train_dataset,
args.batch_size,
shuffle=True,
num_workers=4)
val_loader = DataLoader(val_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
test_loader = DataLoader(test_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
for data in train_loader:
num_features = data.num_features
break
model = GNN_RSR(num_features, hidden_dim=args.hidden_dim).to(device)
model.to(device)
best_val_loss = 999
best_rs = 0
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
for epoch in range(1, args.num_epochs + 1):
start = time.time()
train_loss = train_loop(model, train_loader, optimizer, device)
val_loss, corrs, results_df = test(model, val_loader, device)
if corrs['all_spearman'] > best_rs:
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': train_loss,
}, os.path.join(log_dir, f'best_weights.pt'))
best_rs = corrs['all_spearman']
elapsed = (time.time() - start)
print('Epoch: {:03d}, Time: {:.3f} s'.format(epoch, elapsed))
print(
'\tTrain RMSE: {:.7f}, Val RMSE: {:.7f}, Per-target Spearman R: {:.7f}, Global Spearman R: {:.7f}'
.format(train_loss, val_loss, corrs['per_target_spearman'],
corrs['all_spearman']))
if test_mode:
test_file = os.path.join(log_dir, f'rsr_rep{rep}.csv')
model.load_state_dict(
torch.load(os.path.join(log_dir, f'best_weights.pt')))
val_loss, corrs, results_df = test(model, test_loader, device)
# plot_corr(y_true, y_pred, os.path.join(log_dir, f'corr_{split}_test.png'))
print(
'\tTest RMSE: {:.7f}, Per-target Spearman R: {:.7f}, Global Spearman R: {:.7f}'
.format(train_loss, val_loss, corrs['per_target_spearman'],
corrs['all_spearman']))
pd.to_csv(results_df, test_file, index=False)
def __init__(self, lmdb_path):
self.dataset = LMDBDataset(lmdb_path)
def train(args, device, log_dir, seed=None, test_mode=False):
# logger = logging.getLogger('lba')
# logger.basicConfig(filename=os.path.join(log_dir, f'train_{split}_cv{fold}.log'),level=logging.INFO)
train_dataset = LMDBDataset(os.path.join(args.data_dir, 'train'),
transform=GNNTransformPSR())
val_dataset = LMDBDataset(os.path.join(args.data_dir, 'val'),
transform=GNNTransformPSR())
test_dataset = LMDBDataset(os.path.join(args.data_dir, 'test'),
transform=GNNTransformPSR())
train_loader = DataLoader(train_dataset,
args.batch_size,
shuffle=True,
num_workers=4)
val_loader = DataLoader(val_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
test_loader = DataLoader(test_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
for data in train_loader:
num_features = data.num_features
break
model = GNN_PSR(num_features, hidden_dim=args.hidden_dim).to(device)
model.to(device)
best_val_loss = 999
best_rp = 0
best_rs = 0
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=0.7,
patience=3,
min_lr=0.00001)
for epoch in range(1, args.num_epochs + 1):
start = time.time()
train_loss = train_loop(model, train_loader, optimizer, device)
print('validating...')
val_loss, corrs, test_df = test(model, val_loader, device)
scheduler.step(val_loss)
if corrs['all_spearman'] > best_rs:
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': train_loss,
}, os.path.join(log_dir, f'best_weights.pt'))
best_rs = corrs['all_spearman']
elapsed = (time.time() - start)
print('Epoch: {:03d}, Time: {:.3f} s'.format(epoch, elapsed))
print(
'\tTrain RMSE: {:.7f}, Val RMSE: {:.7f}, Per-target Spearman R: {:.7f}, Global Spearman R: {:.7f}'
.format(train_loss, val_loss, corrs['per_target_spearman'],
corrs['all_spearman']))
if test_mode:
test_file = os.path.join(log_dir, f'test_results.txt')
model.load_state_dict(
torch.load(os.path.join(log_dir, f'best_weights.pt')))
test_loss, corrs, test_df = test(model, val_loader, device)
print(
'Test RMSE: {:.7f}, Per-target Spearman R: {:.7f}, Global Spearman R: {:.7f}'
.format(test_loss, corrs['per_target_spearman'],
corrs['all_spearman']))
test_df.to_csv(test_file)
# Last dimension is atom channel, so we need to move it to the front
# per pytroch style
grid = np.moveaxis(grid, -1, 0)
return grid
def __call__(self, item):
# Transform protein into voxel grids.
# Apply random rotation matrix.
id = eval(item['id'])
transformed = {
'feature': self._voxelize(item['atoms']),
'label': item['scores']['gdt_ts'],
'target': id[0],
'decoy': id[1],
}
return transformed
if __name__ == "__main__":
dataset_path = os.path.join(os.environ['PSR_DATA'], 'val')
dataset = LMDBDataset(dataset_path,
transform=CNN3D_TransformPSR(radius=10.0))
dataloader = DataLoader(dataset, batch_size=8, shuffle=False)
for item in dataloader:
print('feature shape:', item['feature'].shape)
print('label:', item['label'])
print('target:', item['target'])
print('decoy:', item['decoy'])
break
def train(args, device, log_dir, seed=None, test_mode=False):
# logger = logging.getLogger('lba')
# logger.basicConfig(filename=os.path.join(log_dir, f'train_{split}_cv{fold}.log'),level=logging.INFO)
train_dataset = LMDBDataset(os.path.join(args.data_dir, 'train'),
transform=GNNTransformLBA())
val_dataset = LMDBDataset(os.path.join(args.data_dir, 'val'),
transform=GNNTransformLBA())
test_dataset = LMDBDataset(os.path.join(args.data_dir, 'test'),
transform=GNNTransformLBA())
train_loader = DataLoader(train_dataset,
args.batch_size,
shuffle=True,
num_workers=4)
val_loader = DataLoader(val_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
test_loader = DataLoader(test_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
for data in train_loader:
num_features = data.num_features
break
model = GNN_LBA(num_features, hidden_dim=args.hidden_dim).to(device)
model.to(device)
best_val_loss = 999
best_rp = 0
best_rs = 0
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
for epoch in range(1, args.num_epochs + 1):
start = time.time()
train_loss = train_loop(model, train_loader, optimizer, device)
val_loss, r_p, r_s, y_true, y_pred = test(model, val_loader, device)
if val_loss < best_val_loss:
save_weights(model, os.path.join(log_dir, f'best_weights.pt'))
# plot_corr(y_true, y_pred, os.path.join(log_dir, f'corr_{split}.png'))
best_val_loss = val_loss
best_rp = r_p
best_rs = r_s
elapsed = (time.time() - start)
print('Epoch: {:03d}, Time: {:.3f} s'.format(epoch, elapsed))
print(
'\tTrain RMSE: {:.7f}, Val RMSE: {:.7f}, Pearson R: {:.7f}, Spearman R: {:.7f}'
.format(train_loss, val_loss, r_p, r_s))
# logger.info('{:03d}\t{:.7f}\t{:.7f}\t{:.7f}\t{:.7f}\n'.format(epoch, train_loss, val_loss, r_p, r_s))
if test_mode:
test_file = os.path.join(log_dir, f'test_results.txt')
model.load_state_dict(
torch.load(os.path.join(log_dir, f'best_weights.pt')))
rmse, pearson, spearman, y_true, y_pred = test(model, test_loader,
device)
# plot_corr(y_true, y_pred, os.path.join(log_dir, f'corr_{split}_test.png'))
print(
'Test RMSE: {:.7f}, Pearson R: {:.7f}, Spearman R: {:.7f}'.format(
rmse, pearson, spearman))
with open(test_file, 'a+') as out:
out.write('{}\t{:.7f}\t{:.7f}\t{:.7f}\n'.format(
seed, rmse, pearson, spearman))
return best_val_loss, best_rp, best_rs
'h298_atom',
'g298_atom',
'cv_atom',
]
self.label_mapping = {k: v for v, k in enumerate(label_mapping)}
return item['labels'][self.label_mapping[name]]
def __call__(self, item):
# Transform molecule into voxel grids.
# Apply random rotation matrix.
transformed = {
'feature': self._voxelize(item['atoms']),
'label': self._lookup_label(item, self.label_name),
'id': item['id'],
}
return transformed
if __name__ == "__main__":
dataset_path = os.path.join(os.environ['SMP_DATA'], 'val')
dataset = LMDBDataset(dataset_path,
transform=CNN3D_TransformSMP(label_name='alpha',
radius=10.0))
dataloader = DataLoader(dataset, batch_size=8, shuffle=False)
for item in dataloader:
print('feature shape:', item['feature'].shape)
print('label:', item['label'])
print('id:', item['id'])
break
def train(args, device, log_dir, rep=None, test_mode=False):
# logger = logging.getLogger('lba')
# logger.basicConfig(filename=os.path.join(log_dir, f'train_{split}_cv{fold}.log'),level=logging.INFO)
transform = GNNTransformMSP()
if args.precomputed:
train_dataset = PTGDataset(os.path.join(args.data_dir, 'train'))
val_dataset = PTGDataset(os.path.join(args.data_dir, 'val'))
test_dataset = PTGDataset(os.path.join(args.data_dir, 'val'))
else:
train_dataset = LMDBDataset(os.path.join(args.data_dir, 'train'),
transform=transform)
val_dataset = LMDBDataset(os.path.join(args.data_dir, 'val'),
transform=transform)
test_dataset = LMDBDataset(os.path.join(args.data_dir, 'test'),
transform=transform)
train_loader = DataLoader(
train_dataset,
args.batch_size,
shuffle=True,
num_workers=4,
collate_fn=CollaterMSP(batch_size=args.batch_size))
val_loader = DataLoader(val_dataset,
args.batch_size,
shuffle=False,
num_workers=4,
collate_fn=CollaterMSP(batch_size=args.batch_size))
test_loader = DataLoader(
test_dataset,
args.batch_size,
shuffle=False,
num_workers=4,
collate_fn=CollaterMSP(batch_size=args.batch_size))
for original, mutated in train_loader:
num_features = original.num_features
break
gcn_model = GNN_MSP(num_features, hidden_dim=args.hidden_dim).to(device)
gcn_model.to(device)
ff_model = MLP_MSP(args.hidden_dim).to(device)
best_val_loss = 999
best_val_auroc = 0
params = [x for x in gcn_model.parameters()
] + [x for x in ff_model.parameters()]
criterion = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([4]))
criterion.to(device)
optimizer = torch.optim.Adam(params, lr=args.learning_rate)
for epoch in range(1, args.num_epochs + 1):
start = time.time()
train_loss = train_loop(epoch, gcn_model, ff_model, train_loader,
criterion, optimizer, device)
print('validating...')
val_loss, auroc, auprc, _, _ = test(gcn_model, ff_model, val_loader,
criterion, device)
if auroc > best_val_auroc:
torch.save(
{
'epoch': epoch,
'gcn_state_dict': gcn_model.state_dict(),
'ff_state_dict': ff_model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': train_loss,
}, os.path.join(log_dir, f'best_weights_rep{rep}.pt'))
best_val_auroc = auroc
elapsed = (time.time() - start)
print('Epoch: {:03d}, Time: {:.3f} s'.format(epoch, elapsed))
print(
f'\tTrain loss {train_loss}, Val loss {val_loss}, Val AUROC {auroc}, Val auprc {auprc}'
)
if test_mode:
train_file = os.path.join(log_dir, f'msp-rep{rep}.best.train.pt')
val_file = os.path.join(log_dir, f'msp-rep{rep}.best.val.pt')
test_file = os.path.join(log_dir, f'msp-rep{rep}.best.test.pt')
cpt = torch.load(os.path.join(log_dir, f'best_weights_rep{rep}.pt'))
gcn_model.load_state_dict(cpt['gcn_state_dict'])
ff_model.load_state_dict(cpt['ff_state_dict'])
_, _, _, y_true_train, y_pred_train = test(gcn_model, ff_model,
train_loader, criterion,
device)
torch.save({
'targets': y_true_train,
'predictions': y_pred_train
}, train_file)
_, _, _, y_true_val, y_pred_val = test(gcn_model, ff_model, val_loader,
criterion, device)
torch.save({
'targets': y_true_val,
'predictions': y_pred_val
}, val_file)
test_loss, auroc, auprc, y_true_test, y_pred_test = test(
gcn_model, ff_model, test_loader, criterion, device)
print(
f'\tTest loss {test_loss}, Test AUROC {auroc}, Test auprc {auprc}')
torch.save({
'targets': y_true_test,
'predictions': y_pred_test
}, test_file)
return test_loss, auroc, auprc
return best_val_loss
graph = item['atoms']
x2 = torch.tensor(item['atom_feats'],
dtype=torch.float).t().contiguous()
graph.x = torch.cat([graph.x.to(torch.float), x2], dim=-1)
graph.y = self._lookup_label(item, self.label_name)
graph.id = item['id']
return graph
if __name__ == "__main__":
save_dir = '/scratch/users/aderry/atom3d/smp'
data_dir = '/scratch/users/aderry/lmdb/atom3d/small_molecule_properties/splits/split-randomly/data'
os.makedirs(os.path.join(save_dir, 'train'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'val'), exist_ok=True)
os.makedirs(os.path.join(save_dir, 'test'), exist_ok=True)
train_dataset = LMDBDataset(os.path.join(data_dir, 'train'),
transform=GNNTransformSMP(label_name='mu'))
# val_dataset = LMDBDataset(os.path.join(data_dir, 'val'), transform=GNNTransformSMP())
# test_dataset = LMDBDataset(os.path.join(data_dir, 'test'), transform=GNNTransformSMP())
# train_loader = DataLoader(train_dataset, 1, shuffle=True, num_workers=4)
# val_loader = DataLoader(val_dataset, 1, shuffle=False, num_workers=4)
# test_loader = DataLoader(test_dataset, 1, shuffle=False, num_workers=4)
# for item in dataset[0]:
# print(item, type(dataset[0][item]))
for i, item in enumerate(tqdm(train_dataset)):
print(item.y)
# torch.save(item, os.path.join(save_dir, 'train', f'data_{i}.pt'))
# for i, item in enumerate(tqdm(val_dataset)):
# torch.save(item, os.path.join(save_dir, 'val', f'data_{i}.pt'))
label_key='scores')
else:
item = prot_graph_transform(
item,
atom_keys=['atoms_protein', 'atoms_pocket'],
label_key='scores')
# transform ligand into PTG graph
item = mol_graph_transform(item,
'atoms_ligand',
'scores',
use_bonds=True)
node_feats, edges, edge_feats, node_pos = gr.combine_graphs(
item['atoms_pocket'], item['atoms_ligand'])
combined_graph = Data(node_feats,
edges,
edge_feats,
y=item['scores']['neglog_aff'],
pos=node_pos)
return combined_graph
if __name__ == "__main__":
dataset = LMDBDataset(
'/scratch/users/aderry/lmdb/atom3d/lba_lmdb/splits/split-by-sequence-identity-30/data/train',
transform=GNNTransformLBA())
dataloader = DataLoader(dataset, batch_size=1, shuffle=False)
# for item in dataset[0]:
# print(item, type(dataset[0][item]))
for item in dataloader:
print(item)
break
def train(args, device, log_dir, rep=None, test_mode=False):
# logger = logging.getLogger('lba')
# logger.basicConfig(filename=os.path.join(log_dir, f'train_{split}_cv{fold}.log'),level=logging.INFO)
if args.precomputed:
train_dataset = PTGDataset(os.path.join(args.data_dir, 'train'))
val_dataset = PTGDataset(os.path.join(args.data_dir, 'val'))
test_dataset = PTGDataset(os.path.join(args.data_dir, 'val'))
else:
transform = GNNTransformLBA()
train_dataset = LMDBDataset(os.path.join(args.data_dir, 'train'),
transform=transform)
val_dataset = LMDBDataset(os.path.join(args.data_dir, 'val'),
transform=transform)
test_dataset = LMDBDataset(os.path.join(args.data_dir, 'test'),
transform=transform)
train_loader = DataLoader(train_dataset,
args.batch_size,
shuffle=True,
num_workers=4)
val_loader = DataLoader(val_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
test_loader = DataLoader(test_dataset,
args.batch_size,
shuffle=False,
num_workers=4)
for data in train_loader:
num_features = data.num_features
break
model = GNN_LBA(num_features, hidden_dim=args.hidden_dim).to(device)
model.to(device)
best_val_loss = 999
best_rp = 0
best_rs = 0
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
for epoch in range(1, args.num_epochs + 1):
start = time.time()
train_loss = train_loop(model, train_loader, optimizer, device)
val_loss, r_p, r_s, y_true, y_pred = test(model, val_loader, device)
if val_loss < best_val_loss:
torch.save(
{
'epoch': epoch,
'model_state_dict': model.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': train_loss,
}, os.path.join(log_dir, f'best_weights_rep{rep}.pt'))
# plot_corr(y_true, y_pred, os.path.join(log_dir, f'corr_{split}.png'))
best_val_loss = val_loss
best_rp = r_p
best_rs = r_s
elapsed = (time.time() - start)
print('Epoch: {:03d}, Time: {:.3f} s'.format(epoch, elapsed))
print(
'\tTrain RMSE: {:.7f}, Val RMSE: {:.7f}, Pearson R: {:.7f}, Spearman R: {:.7f}'
.format(train_loss, val_loss, r_p, r_s))
# logger.info('{:03d}\t{:.7f}\t{:.7f}\t{:.7f}\t{:.7f}\n'.format(epoch, train_loss, val_loss, r_p, r_s))
if test_mode:
train_file = os.path.join(log_dir, f'lba-rep{rep}.best.train.pt')
val_file = os.path.join(log_dir, f'lba-rep{rep}.best.val.pt')
test_file = os.path.join(log_dir, f'lba-rep{rep}.best.test.pt')
cpt = torch.load(os.path.join(log_dir, f'best_weights_rep{rep}.pt'))
model.load_state_dict(cpt['model_state_dict'])
_, _, _, y_true_train, y_pred_train = test(model, train_loader, device)
torch.save({
'targets': y_true_train,
'predictions': y_pred_train
}, train_file)
_, _, _, y_true_val, y_pred_val = test(model, val_loader, device)
torch.save({
'targets': y_true_val,
'predictions': y_pred_val
}, val_file)
rmse, pearson, spearman, y_true_test, y_pred_test = test(
model, test_loader, device)
print(
f'\tTest RMSE {rmse}, Test Pearson {pearson}, Test Spearman {spearman}'
)
torch.save({
'targets': y_true_test,
'predictions': y_pred_test
}, test_file)
return best_val_loss, best_rp, best_rs