moreo
/
kernel_authorship
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Alejandro Moreo Fernandez 2021-02-15 16:23:38 +01:00
parent b1160c5336
commit f58d8c54b5
2 changed files with 14 additions and 21 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

22
src/main.py
View File

@ -57,7 +57,7 @@ def instantiate_model(A, index, pad_index, device):
channels_out=opt.chout,
kernel_sizes=opt.kernelsizes),
ff=FFProjection(input_size=len(opt.kernelsizes) * opt.chout,
hidden_sizes=[],
hidden_sizes=[512],
output_size=opt.repr,
activation=nn.functional.relu,
dropout=0.5,
@ -110,8 +110,7 @@ def main(opt):
checkpointpath=opt.checkpoint)
# svm_experiment(cls.project(Xtr), ytr, cls.project(Xte), yte, foo, 'svm-pre')
svm_experiment(cls.project_kernel(Xtr), ytr, cls.project_kernel(Xte), yte, foo, 'svm-kernel')
Xtr_svm, Xte_svm = cls.project_kernel(Xtr), cls.project_kernel(Xte)
val_microf1 = cls.train_linear_classifier(Xtr_, ytr_, Xval_, yval_,
batch_size=opt.batchsize, epochs=opt.epochs, lr=opt.lr,
@ -124,7 +123,7 @@ def main(opt):
acc, macrof1, microf1 = evaluation(yte, yte_)
foo.write(f'sav(fix)-lin(trained) network prediction: acc={acc:.3f} macrof1={macrof1:.3f} microf1={microf1:.3f}\n')
val_microf1 = cls.fit(Xtr, ytr,
val_microf1 = cls.fit(Xtr_, ytr_, Xval_, yval_,
batch_size=opt.batchsize, epochs=opt.epochs, alpha=opt.alpha, lr=opt.lr,
log=f'{opt.log}/{method}-{dataset_name}.csv',
checkpointpath=opt.checkpoint
@ -139,7 +138,7 @@ def main(opt):
print('training end-to-end without self-supervision init')
cls, phi = instantiate_model(A, index, pad_index, device)
# train
val_microf1 = cls.fit(Xtr, ytr,
val_microf1 = cls.fit(Xtr_, ytr_, Xval_, yval_,
batch_size=opt.batchsize, epochs=opt.epochs, alpha=opt.alpha, lr=opt.lr,
log=f'{opt.log}/{method}-{dataset_name}.csv',
checkpointpath=opt.checkpoint
@ -149,6 +148,8 @@ def main(opt):
print('end-to-end (w/o self-supervised initialization) network prediction')
acc, macrof1, microf1 = evaluation(yte, yte_)
svm_experiment(Xtr_svm, ytr, Xte_svm, yte, foo, 'svm-kernel')
# results = Results(opt.output)
# results.add(dataset_name, method, acc, macrof1, microf1, val_microf1)
@ -186,6 +187,7 @@ class Results:
def close(self):
self.foo.close()
def svm_experiment(Xtr, ytr, Xte, yte, foo, name):
svm = GridSearchCV(
LinearSVC(), param_grid={'C': np.logspace(-2, 3, 6), 'class_weight': ['balanced', None]}, n_jobs=-1
@ -199,13 +201,13 @@ def svm_experiment(Xtr, ytr, Xte, yte, foo, name):
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='CNN with KTA regularization')
parser.add_argument('-H', '--hidden', help='Hidden/embedding size', type=int, default=32)
parser.add_argument('-H', '--hidden', help='Hidden/embedding size', type=int, default=16)
parser.add_argument('-c', '--chout', help='Channels output size', type=int, default=128)
parser.add_argument('-r', '--repr', help='Projection size (phi)', type=int, default=2048)
parser.add_argument('-r', '--repr', help='Projection size (phi)', type=int, default=256)
parser.add_argument('-k', '--kernelsizes', help='Size of the convolutional kernels', nargs='+', default=[6,7,8])
parser.add_argument('-p', '--pad', help='Pad length', type=int, default=3000)
parser.add_argument('-b', '--batchsize', help='Batch size', type=int, default=250)
parser.add_argument('-e', '--epochs', help='Max number of epochs', type=int, default=250)
parser.add_argument('-b', '--batchsize', help='Batch size', type=int, default=100)
parser.add_argument('-e', '--epochs', help='Max number of epochs', type=int, default=500)
parser.add_argument('-A', '--authors', help='Number of authors (-1 to select all)', type=int, default=-1)
parser.add_argument('-D', '--documents', help='Number of documents per author (-1 to select all)', type=int, default=-1)
parser.add_argument('-s', '--seed', help='Random seed', type=int, default=0)
@ -215,7 +217,7 @@ if __name__ == '__main__':
'This parameter indicates a directory, the name of the pickle is '
'derived automatically.', default='../pickles')
parser.add_argument('-a', '--alpha', help='Controls the loss as attr-loss(alpha) + sav-loss(1-alpha)', type=float, default=1.)
parser.add_argument('--lr', help='Learning rate', type=float, default=0.01)
parser.add_argument('--lr', help='Learning rate', type=float, default=0.001)
parser.add_argument('--checkpoint', help='Path where to dump model parameters', default='../checkpoint/model.dat')
parser.add_argument('-n', '--name', help='Name of the model', default='auto')
requiredNamed = parser.add_argument_group('required named arguments')

13
src/model/classifiers.py
View File

@ -30,7 +30,7 @@ class AuthorshipAttributionClassifier(nn.Module):
if p.dim() > 1 and p.requires_grad:
nn.init.xavier_uniform_(p)
def fit(self, X, y, batch_size, epochs, patience=50, lr=0.001, val_prop=0.1, alpha=1., log='../log/tmp.csv', checkpointpath='../checkpoint/model.dat'):
def fit(self, X, y, Xval, yval, batch_size, epochs, patience=20, lr=0.001, alpha=1., log='../log/tmp.csv', checkpointpath='../checkpoint/model.dat'):
assert 0 <= alpha <= 1, 'wrong range, alpha must be in [0,1]'
early_stop = EarlyStop(patience)
@ -39,8 +39,6 @@ class AuthorshipAttributionClassifier(nn.Module):
savcriterion = torch.nn.BCEWithLogitsLoss().to(self.device)
optim = torch.optim.Adam(self.parameters(), lr=lr)
X, Xval, y, yval = train_test_split(X, y, test_size=val_prop, stratify=y)
tr_data = IndexedDataset(X, y, self.pad_length, self.pad_index, self.device)
val_data = IndexedDataset(Xval, yval, self.pad_length, self.pad_index, self.device)
@ -99,16 +97,14 @@ class AuthorshipAttributionClassifier(nn.Module):
f'loss={tr_loss:.5f} '
f'attr-loss={np.mean(attr_losses):.5f} '
f'sav-loss={np.mean(sav_losses):.5f} '
f'val_loss={val_loss:.5f} val_acc={acc:.4f} macrof1={macrof1:.4f} microf1={microf1:.4f}'
f'val_loss={val_loss:.5f} val_acc={acc:.4f} macrof1={macrof1:.4f} microf1={microf1:.4f} '
f'patience={early_stop.patience}/{early_stop.patience_limit}')
# validation
self.eval()
with torch.no_grad():
predictions, losses = [], []
# for xi, yi in batcher_val.epoch(Xval, yval):
for xi, yi in val_data.asDataLoader(batch_size, shuffle=False):
# xi = self.padder.transform(xi)
logits = self.forward(xi)
loss = criterion(logits, torch.as_tensor(yi).to(self.device))
losses.append(loss.item())
@ -137,7 +133,6 @@ class AuthorshipAttributionClassifier(nn.Module):
early_stop = EarlyStop(patience, lower_is_better=True)
criterion = SupConLoss1View().to(self.device)
# criterion = SupConLoss1ViewCrossEntropy().to(self.device)
optim = torch.optim.Adam(self.parameters(), lr=lr)
tr_data = IndexedDataset(X, y, self.pad_length, self.pad_index, self.device)
@ -153,14 +148,11 @@ class AuthorshipAttributionClassifier(nn.Module):
self.train()
losses, pos_losses, neg_losses = [], [], []
for xi, yi in tr_data.asDataLoader(batch_size, shuffle=True):
#while True:
optim.zero_grad()
phi = self.projector(xi)
phi = self.linear_proj(phi)
phi = F.normalize(phi, p=2, dim=-1)
#contrastive_loss = criterion(phi, torch.as_tensor(yi).to(self.device))
contrastive_loss, neg_loss, pos_loss = criterion(phi, torch.as_tensor(yi).to(self.device))
#contrastive_loss = neg_loss+pos_loss
contrastive_loss.backward()
optim.step()
losses.append(contrastive_loss.item())
@ -180,7 +172,6 @@ class AuthorshipAttributionClassifier(nn.Module):
for xi, yi in val_data.asDataLoader(batch_size, shuffle=False):
phi = self.projector(xi)
contrastive_loss, neg_loss, pos_loss = criterion(phi, torch.as_tensor(yi).to(self.device))
#contrastive_loss = neg_loss + pos_loss
losses.append((neg_loss + pos_loss).item())
neg_losses_val.append(neg_loss.item())
pos_losses_val.append(pos_loss.item())