def predict_contacts(model, x, y, use_cuda):
b = len(x)
x,order = pack_sequences(x)
x = PackedSequence(Variable(x.data), x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
logits = []
y_list = []
for i in range(b):
zi = z[i]
lp = model.predict(zi.unsqueeze(0)).view(-1)
yi = y[i].view(-1)
if use_cuda:
yi = yi.cuda()
mask = (yi < 0)
lp = lp[~mask]
yi = yi[~mask]
logits.append(lp)
y_list.append(yi)
return logits, y_list
def peptide_embedding(peptide_list, max_len, pretrained_model):
#peptide_list = peptide_encoding(peptides)
c = [torch.from_numpy(peptide).long() for peptide in peptide_list]
c,order = pack_sequences(c)
# Load trained full SSA model for structure-based protein embeddings
features = pretrained_model
# torch.load('pretrained_models/ssa_L1_100d_lstm3x512_lm_i512_mb64_tau0.5_lambda0.1_p0.05_epoch100.sav')
#features.eval()
z = features(c)
z = unpack_sequences(z, order)
# pad sequences to be of same sequnece length
maxLength = max_len
Embedding_List = []
for elem in z:
diff = maxLength - elem.shape[0]
elem = F.pad(elem, (0, 0, 0, diff), 'constant')
Embedding_List.append(elem)
# Embedding_List = []
# for embedding in z:
# Embedding_List.append(embedding.detach().numpy())
return Embedding_List
def eval_similarity(model, test_iterator, use_cuda):
y = []
logits = []
for x0,x1,y_mb in test_iterator:
if use_cuda:
y_mb = y_mb.cuda()
y.append(y_mb.long())
b = len(x0)
x = x0 + x1
x,order = pack_sequences(x)
x = PackedSequence(Variable(x.data), x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
z0 = z[:b]
z1 = z[b:]
for i in range(b):
z_a = z0[i]
z_b = z1[i]
logits.append(model.score(z_a, z_b))
y = torch.cat(y, 0)
logits = torch.stack(logits, 0)
p = F.sigmoid(logits).data
ones = p.new(p.size(0),1).zero_() + 1
p_ge = torch.cat([ones, p], 1)
p_lt = torch.cat([1-p, ones], 1)
p = p_ge*p_lt
p = p/p.sum(1,keepdim=True) # make sure p is normalized
loss = F.cross_entropy(p, y).item()
_,y_hard = torch.max(p, 1)
levels = torch.arange(5).to(p.device)
y_hat = torch.sum(p*levels, 1)
accuracy = torch.mean((y == y_hard).float()).item()
mse = torch.mean((y.float() - y_hat)**2).item()
y = y.cpu().numpy()
y_hat = y_hat.cpu().numpy()
r,_ = pearsonr(y_hat, y)
rho,_ = spearmanr(y_hat, y)
return loss, accuracy, mse, r, rho
def predict_minibatch(model, x, use_cuda):
b = len(x)
x, order = pack_sequences(x)
x = PackedSequence(x.data, x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
logits = []
for i in range(b):
zi = z[i]
lp = model.predict(zi.unsqueeze(0)).view(zi.size(0), zi.size(0))
logits.append(lp)
return logits
def __call__(self, x):
c = [torch.from_numpy(x_).long() for x_ in x]
c, order = pack_sequences(c)
if self.use_cuda:
c = c.cuda()
if self.full_features:
z = featurize(c, self.lm_embed, self.lstm_stack, self.proj)
else:
z = self.model(c) # embed the sequences
z = unpack_sequences(z, order)
return z
def similarity_grad(model, x0, x1, y, use_cuda, weight=0.5):
if use_cuda:
y = y.cuda()
y = Variable(y)
b = len(x0)
x = x0 + x1
x,order = pack_sequences(x)
x = PackedSequence(Variable(x.data), x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
z0 = z[:b]
z1 = z[b:]
logits = []
for i in range(b):
z_a = z0[i]
z_b = z1[i]
logits.append(model.score(z_a, z_b))
logits = torch.stack(logits, 0)
loss = F.binary_cross_entropy_with_logits(logits, y.float())
# backprop weighted loss
w_loss = loss*weight
w_loss.backward()
# calculate minibatch performance metrics
with torch.no_grad():
p = F.sigmoid(logits)
ones = p.new(b,1).zero_() + 1
p_ge = torch.cat([ones, p], 1)
p_lt = torch.cat([1-p, ones], 1)
p = p_ge*p_lt
p = p/p.sum(1,keepdim=True) # make sure p is normalized
_,y_hard = torch.max(p, 1)
levels = torch.arange(5).to(p.device)
y_hat = torch.sum(p*levels, 1)
y = torch.sum(y.data, 1)
loss = F.cross_entropy(p, y).item() # calculate cross entropy loss from p vector
correct = torch.sum((y == y_hard).float()).item()
mse = torch.mean((y.float() - y_hat)**2).item()
return loss, correct, mse, b
def __call__(self, x, y):
n = len(x)
c = [torch.from_numpy(x_).long()
for x_ in x] + [torch.from_numpy(y_).long() for y_ in y]
c, order = pack_sequences(c)
if self.use_cuda:
c = c.cuda()
with torch.no_grad():
z = self.model(c) # embed the sequences
z = unpack_sequences(z, order)
scores = np.zeros(n)
if self.mode == 'align':
for i in range(n):
z_x = z[i]
z_y = z[i + n]
logits = self.model.score(z_x, z_y)
p = F.sigmoid(logits).cpu()
p_ge = torch.ones(p.size(0) + 1)
p_ge[1:] = p
p_lt = torch.ones(p.size(0) + 1)
p_lt[:-1] = 1 - p
p = p_ge * p_lt
p = p / p.sum() # make sure p is normalized
levels = torch.arange(5).float()
scores[i] = torch.sum(p * levels).item()
elif self.mode == 'coarse':
z_x = z[:n]
z_y = z[n:]
z_x = torch.stack([z.mean(0) for z in z_x], 0)
z_y = torch.stack([z.mean(0) for z in z_y], 0)
scores[:] = -torch.sum(torch.abs(z_x - z_y), 1).cpu().numpy()
return scores
def contacts_grad(model, x, y, use_cuda, weight=0.5):
b = len(x)
x,order = pack_sequences(x)
x = PackedSequence(Variable(x.data), x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
logits = []
for i in range(b):
zi = z[i]
lp = model.predict(zi.unsqueeze(0)).view(-1)
logits.append(lp)
logits = torch.cat(logits, 0)
y = torch.cat([yi.view(-1) for yi in y])
if use_cuda:
y = y.cuda()
mask = (y < 0)
logits = logits[~mask]
y = Variable(y[~mask])
b = y.size(0)
loss = F.binary_cross_entropy_with_logits(logits, y)
# backprop weighted loss
w_loss = loss*weight
w_loss.backward()
# calculate the recall and precision
with torch.no_grad():
p_hat = F.sigmoid(logits)
tp = torch.sum(p_hat*y).item()
gp = y.sum().item()
pp = p_hat.sum().item()
return loss.item(), tp, gp, pp, b
def main():
import argparse
parser = argparse.ArgumentParser(
'Script for training embedding model on SCOP.')
parser.add_argument('--dev',
action='store_true',
help='use train/dev split')
parser.add_argument(
'-m',
'--model',
choices=['ssa', 'ua', 'me'],
default='ssa',
help=
'alignment scoring method for comparing sequences in embedding space [ssa: soft symmetric alignment, ua: uniform alignment, me: mean embedding] (default: ssa)'
)
parser.add_argument('--allow-insert',
action='store_true',
help='model insertions (default: false)')
parser.add_argument('--norm',
choices=['l1', 'l2'],
default='l1',
help='comparison norm (default: l1)')
parser.add_argument('--rnn-type',
choices=['lstm', 'gru'],
default='lstm',
help='type of RNN block to use (default: lstm)')
parser.add_argument('--embedding-dim',
type=int,
default=100,
help='embedding dimension (default: 100)')
parser.add_argument('--input-dim',
type=int,
default=512,
help='dimension of input to RNN (default: 512)')
parser.add_argument('--rnn-dim',
type=int,
default=512,
help='hidden units of RNNs (default: 512)')
parser.add_argument('--num-layers',
type=int,
default=3,
help='number of RNN layers (default: 3)')
parser.add_argument('--dropout',
type=float,
default=0,
help='dropout probability (default: 0)')
parser.add_argument('--epoch-size',
type=int,
default=100000,
help='number of examples per epoch (default: 100,000)')
parser.add_argument('--epoch-scale',
type=int,
default=5,
help='scaling on epoch size (default: 5)')
parser.add_argument('--num-epochs',
type=int,
default=100,
help='number of epochs (default: 100)')
parser.add_argument('--batch-size',
type=int,
default=64,
help='minibatch size (default: 64)')
parser.add_argument('--weight-decay',
type=float,
default=0,
help='L2 regularization (default: 0)')
parser.add_argument('--lr', type=float, default=0.001)
parser.add_argument('--tau',
type=float,
default=0.5,
help='sampling proportion exponent (default: 0.5)')
parser.add_argument(
'--augment',
type=float,
default=0,
help=
'probability of resampling amino acid for data augmentation (default: 0)'
)
parser.add_argument('--lm',
help='pretrained LM to use as initial embedding')
parser.add_argument('-o',
'--output',
help='output file path (default: stdout)')
parser.add_argument('--save-prefix', help='path prefix for saving models')
parser.add_argument('-d',
'--device',
type=int,
default=-2,
help='compute device to use')
args = parser.parse_args()
prefix = args.output
## set the device
d = args.device
use_cuda = (d != -1) and torch.cuda.is_available()
if d >= 0:
torch.cuda.set_device(d)
## make the datasets
astral_train_path = 'data/SCOPe/astral-scopedom-seqres-gd-sel-gs-bib-95-2.06.train.fa'
astral_testpairs_path = 'data/SCOPe/astral-scopedom-seqres-gd-sel-gs-bib-95-2.06.test.sampledpairs.txt'
if args.dev:
astral_train_path = 'data/SCOPe/astral-scopedom-seqres-gd-sel-gs-bib-95-2.06.train.train.fa'
astral_testpairs_path = 'data/SCOPe/astral-scopedom-seqres-gd-sel-gs-bib-95-2.06.train.dev.sampledpairs.txt'
alphabet = Uniprot21()
print('# loading training sequences:', astral_train_path, file=sys.stderr)
with open(astral_train_path, 'rb') as f:
names_train, structs_train, sequences_train = scop.parse_astral(
f, encoder=alphabet)
x_train = [torch.from_numpy(x).long() for x in sequences_train]
if use_cuda:
x_train = [x.cuda() for x in x_train]
y_train = torch.from_numpy(structs_train)
print('# loaded', len(x_train), 'training sequences', file=sys.stderr)
print('# loading test sequence pairs:',
astral_testpairs_path,
file=sys.stderr)
test_pairs_table = pd.read_csv(astral_testpairs_path, sep='\t')
x0_test = [
x.encode('utf-8').upper() for x in test_pairs_table['sequence_A']
]
x0_test = [torch.from_numpy(alphabet.encode(x)).long() for x in x0_test]
x1_test = [
x.encode('utf-8').upper() for x in test_pairs_table['sequence_B']
]
x1_test = [torch.from_numpy(alphabet.encode(x)).long() for x in x1_test]
if use_cuda:
x0_test = [x.cuda() for x in x0_test]
x1_test = [x.cuda() for x in x1_test]
y_test = test_pairs_table['similarity'].values
y_test = torch.from_numpy(y_test).long()
dataset_test = PairedDataset(x0_test, x1_test, y_test)
print('# loaded', len(x0_test), 'test pairs', file=sys.stderr)
## make the dataset iterators
scale = args.epoch_scale
epoch_size = args.epoch_size
batch_size = args.batch_size
# precompute the similarity pairs
y_train_levels = torch.cumprod(
(y_train.unsqueeze(1) == y_train.unsqueeze(0)).long(), 2)
# data augmentation by resampling amino acids
augment = None
p = 0
if args.augment > 0:
p = args.augment
trans = torch.ones(len(alphabet), len(alphabet))
trans = trans / trans.sum(1, keepdim=True)
if use_cuda:
trans = trans.cuda()
augment = MultinomialResample(trans, p)
print('# resampling amino acids with p:', p, file=sys.stderr)
dataset_train = AllPairsDataset(x_train, y_train_levels, augment=augment)
similarity = y_train_levels.numpy().sum(2)
levels, counts = np.unique(similarity, return_counts=True)
order = np.argsort(levels)
levels = levels[order]
counts = counts[order]
print('#', levels, file=sys.stderr)
print('#', counts / np.sum(counts), file=sys.stderr)
weight = counts**0.5
print('#', weight / np.sum(weight), file=sys.stderr)
weight = counts**0.33
print('#', weight / np.sum(weight), file=sys.stderr)
weight = counts**0.25
print('#', weight / np.sum(weight), file=sys.stderr)
tau = args.tau
print('# using tau:', tau, file=sys.stderr)
print('#', counts**tau / np.sum(counts**tau), file=sys.stderr)
weights = counts**tau / counts
weights = weights[similarity].ravel()
#weights = np.ones(len(dataset_train))
sampler = torch.utils.data.sampler.WeightedRandomSampler(
weights, epoch_size)
# two training dataset iterators for sampling pairs of sequences for training
train_iterator = torch.utils.data.DataLoader(
dataset_train,
batch_size=batch_size,
sampler=sampler,
collate_fn=collate_paired_sequences)
test_iterator = torch.utils.data.DataLoader(
dataset_test,
batch_size=batch_size,
collate_fn=collate_paired_sequences)
## initialize the model
rnn_type = args.rnn_type
rnn_dim = args.rnn_dim
num_layers = args.num_layers
embedding_size = args.embedding_dim
input_dim = args.input_dim
dropout = args.dropout
allow_insert = args.allow_insert
print('# initializing model with:', file=sys.stderr)
print('# embedding_size:', embedding_size, file=sys.stderr)
print('# input_dim:', input_dim, file=sys.stderr)
print('# rnn_dim:', rnn_dim, file=sys.stderr)
print('# num_layers:', num_layers, file=sys.stderr)
print('# dropout:', dropout, file=sys.stderr)
print('# allow_insert:', allow_insert, file=sys.stderr)
compare_type = args.model
print('# comparison method:', compare_type, file=sys.stderr)
lm = None
if args.lm is not None:
lm = torch.load(args.lm)
lm.eval()
## do not update the LM parameters
for param in lm.parameters():
param.requires_grad = False
print('# using LM:', args.lm, file=sys.stderr)
if num_layers > 0:
embedding = src.models.embedding.StackedRNN(len(alphabet),
input_dim,
rnn_dim,
embedding_size,
nlayers=num_layers,
dropout=dropout,
lm=lm)
else:
embedding = src.models.embedding.Linear(len(alphabet),
input_dim,
embedding_size,
lm=lm)
if args.norm == 'l1':
norm = src.models.comparison.L1()
print('# norm: l1', file=sys.stderr)
elif args.norm == 'l2':
norm = src.models.comparison.L2()
print('# norm: l2', file=sys.stderr)
model = src.models.comparison.OrdinalRegression(
embedding,
5,
align_method=compare_type,
compare=norm,
allow_insertions=allow_insert)
if use_cuda:
model.cuda()
## setup training parameters and optimizer
num_epochs = args.num_epochs
weight_decay = args.weight_decay
lr = args.lr
print('# training with Adam: lr={}, weight_decay={}'.format(
lr, weight_decay),
file=sys.stderr)
params = [p for p in model.parameters() if p.requires_grad]
optim = torch.optim.Adam(params, lr=lr, weight_decay=weight_decay)
## train the model
print('# training model', file=sys.stderr)
save_prefix = args.save_prefix
output = args.output
if output is None:
output = sys.stdout
else:
output = open(output, 'w')
digits = int(np.floor(np.log10(num_epochs))) + 1
line = '\t'.join(['epoch', 'split', 'loss', 'mse', 'accuracy', 'r', 'rho'])
print(line, file=output)
for epoch in range(num_epochs):
# train epoch
model.train()
it = 0
n = 0
loss_estimate = 0
mse_estimate = 0
acc_estimate = 0
for x0, x1, y in train_iterator: # zip(train_iterator_0, train_iterator_1):
if use_cuda:
y = y.cuda()
y = Variable(y)
b = len(x0)
x = x0 + x1
x, order = pack_sequences(x)
x = PackedSequence(Variable(x.data), x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
z0 = z[:b]
z1 = z[b:]
logits = []
for i in range(b):
z_a = z0[i]
z_b = z1[i]
logits.append(model.score(z_a, z_b))
logits = torch.stack(logits, 0)
loss = F.binary_cross_entropy_with_logits(logits, y.float())
loss.backward()
optim.step()
optim.zero_grad()
model.clip(
) # projected gradient for bounding ordinal regressionn parameters
p = F.sigmoid(logits)
ones = p.new(b, 1).zero_() + 1
p_ge = torch.cat([ones, p], 1)
p_lt = torch.cat([1 - p, ones], 1)
p = p_ge * p_lt
p = p / p.sum(1, keepdim=True) # make sure p is normalized
_, y_hard = torch.max(p, 1)
levels = torch.arange(5).to(p.device)
y_hat = torch.sum(p * levels, 1)
y = torch.sum(y.data, 1)
loss = F.cross_entropy(
p, y) # calculate cross entropy loss from p vector
correct = torch.sum((y == y_hard).float())
mse = torch.sum((y.float() - y_hat)**2)
n += b
delta = b * (loss.item() - loss_estimate)
loss_estimate += delta / n
delta = correct.item() - b * acc_estimate
acc_estimate += delta / n
delta = mse.item() - b * mse_estimate
mse_estimate += delta / n
if (n - b) // 100 < n // 100:
print(
'# [{}/{}] training {:.1%} loss={:.5f}, mse={:.5f}, acc={:.5f}'
.format(epoch + 1, num_epochs, n / epoch_size,
loss_estimate, mse_estimate, acc_estimate),
end='\r',
file=sys.stderr)
print(' ' * 80, end='\r', file=sys.stderr)
line = '\t'.join([
str(epoch + 1).zfill(digits), 'train',
str(loss_estimate),
str(mse_estimate),
str(acc_estimate), '-', '-'
])
print(line, file=output)
output.flush()
# eval and save model
model.eval()
y = []
logits = []
with torch.no_grad():
for x0, x1, y_mb in test_iterator:
if use_cuda:
y_mb = y_mb.cuda()
y.append(y_mb.long())
b = len(x0)
x = x0 + x1
x, order = pack_sequences(x)
x = PackedSequence(Variable(x.data), x.batch_sizes)
z = model(x) # embed the sequences
z = unpack_sequences(z, order)
z0 = z[:b]
z1 = z[b:]
for i in range(b):
z_a = z0[i]
z_b = z1[i]
logits.append(model.score(z_a, z_b))
y = torch.cat(y, 0)
logits = torch.stack(logits, 0)
p = F.sigmoid(logits).data
ones = p.new(p.size(0), 1).zero_() + 1
p_ge = torch.cat([ones, p], 1)
p_lt = torch.cat([1 - p, ones], 1)
p = p_ge * p_lt
p = p / p.sum(1, keepdim=True) # make sure p is normalized
loss = F.cross_entropy(p, y).item()
_, y_hard = torch.max(p, 1)
levels = torch.arange(5).to(p.device)
y_hat = torch.sum(p * levels, 1)
accuracy = torch.mean((y == y_hard).float()).item()
mse = torch.mean((y.float() - y_hat)**2).item()
y = y.cpu().numpy()
y_hat = y_hat.cpu().numpy()
r, _ = pearsonr(y_hat, y)
rho, _ = spearmanr(y_hat, y)
line = '\t'.join([
str(epoch + 1).zfill(digits), 'test',
str(loss),
str(mse),
str(accuracy),
str(r),
str(rho)
])
print(line, file=output)
output.flush()
# save the model
if save_prefix is not None:
save_path = save_prefix + '_epoch' + str(epoch +
1).zfill(digits) + '.sav'
model.cpu()
torch.save(model, save_path)
if use_cuda:
model.cuda()