def predict(args):
encoder, classifiers, Us, Ps, Ns = torch.load(args.load_model)
map(lambda m: m.eval(), [encoder] + classifiers)
# args = argparser.parse_args()
# say(args)
if args.cuda:
map(lambda m: m.cuda(), [encoder] + classifiers)
Us = [ U.cuda() for U in Us ]
Ps = [ P.cuda() for P in Ps ]
Ns = [ N.cuda() for N in Ns ]
say("\nTransferring from %s to %s\n" % (args.train, args.test))
source_train_sets = args.train.split(',')
train_loaders = []
for source in source_train_sets:
filepath = os.path.join(DATA_DIR, "%s_train.svmlight" % (source))
train_dataset = AmazonDataset(filepath)
train_loader = data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0
)
train_loaders.append(train_loader)
test_filepath = os.path.join(DATA_DIR, "%s_test.svmlight" % (args.test))
test_dataset = AmazonDataset(test_filepath)
test_loader = data.DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0
)
say("Corpus loaded.\n")
mats = [Us, Ps, Ns]
(acc, oracle_acc), confusion_mat = evaluate(
encoder, classifiers,
mats,
[train_loaders, test_loader],
args
)
say(colored("Test accuracy/oracle {:.4f}/{:.4f}\n".format(acc, oracle_acc), 'red'))
def predict(args):
encoder, classifier = torch.load(args.load_model)
map(lambda m: m.eval(), [encoder, classifier])
if args.cuda:
map(lambda m: m.cuda(), [encoder, classifier])
test_filepath = os.path.join(DATA_DIR, "%s_train.svmlight" % (args.test))
assert (os.path.exists(test_filepath))
test_dataset = AmazonDataset(test_filepath)
test_loader = data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0)
acc, confusion_mat, _ = evaluate(encoder, classifier, test_loader, args)
say(colored("Test accuracy {:.4f}\n".format(acc), 'red'))
print confusion_mat
def train(args):
''' Training Strategy
Input: source = {S1, S2, ..., Sk}, target = {T}
Train:
Approach 1: fix metric and learn encoder only
Approach 2: learn metric and encoder alternatively
'''
# test_mahalanobis_metric() and return
encoder_class = get_model_class("mlp")
encoder_class.add_config(argparser)
critic_class = get_critic_class(args.critic)
critic_class.add_config(argparser)
args = argparser.parse_args()
say(args)
# encoder is shared across domains
encoder = encoder_class(args)
say("Transferring from %s to %s\n" % (args.train, args.test))
source_train_sets = args.train.split(',')
train_loaders = []
Us = []
Ps = []
Ns = []
Ws = []
Vs = []
# Ms = []
for source in source_train_sets:
filepath = os.path.join(DATA_DIR, "%s_train.svmlight" % (source))
assert (os.path.exists(filepath))
train_dataset = AmazonDataset(filepath)
train_loader = data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0)
train_loaders.append(train_loader)
if args.metric == "biaffine":
U = torch.FloatTensor(encoder.n_d, encoder.n_d)
W = torch.FloatTensor(encoder.n_d, 1)
nn.init.xavier_uniform_(W)
Ws.append(W)
V = torch.FloatTensor(encoder.n_d, 1)
nn.init.xavier_uniform_(V)
Vs.append(V)
else:
U = torch.FloatTensor(encoder.n_d, args.m_rank)
nn.init.xavier_uniform_(U)
Us.append(U)
P = torch.FloatTensor(encoder.n_d, args.m_rank)
nn.init.xavier_uniform_(P)
Ps.append(P)
N = torch.FloatTensor(encoder.n_d, args.m_rank)
nn.init.xavier_uniform_(N)
Ns.append(N)
# Ms.append(U.mm(U.t()))
unl_filepath = os.path.join(DATA_DIR, "%s_train.svmlight" % (args.test))
assert (os.path.exists(unl_filepath))
unl_dataset = AmazonDomainDataset(unl_filepath)
unl_loader = data.DataLoader(unl_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=0)
valid_filepath = os.path.join(DATA_DIR, "%s_dev.svmlight" % (args.test))
if os.path.exists(valid_filepath):
valid_dataset = AmazonDataset(valid_filepath)
valid_loader = data.DataLoader(valid_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0)
else:
valid_loader = None
test_filepath = os.path.join(DATA_DIR, "%s_test.svmlight" % (args.test))
assert (os.path.exists(test_filepath))
test_dataset = AmazonDataset(test_filepath)
test_loader = data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0)
say("Corpus loaded.\n")
classifiers = []
for source in source_train_sets:
classifier = nn.Linear(encoder.n_out, 2) # binary classification
nn.init.xavier_normal_(classifier.weight)
nn.init.constant_(classifier.bias, 0.1)
classifiers.append(classifier)
critic = critic_class(encoder, args)
# if args.save_model:
# say(colored("Save model to {}\n".format(args.save_model + ".init"), 'red'))
# torch.save([encoder, classifiers, Us, Ps, Ns], args.save_model + ".init")
if args.cuda:
# map(lambda m: m.cuda(), [encoder, critic] + classifiers)
encoder = encoder.cuda()
critic = critic.cuda()
classifiers = [x.cuda() for x in classifiers]
Us = [Variable(U.cuda(), requires_grad=True) for U in Us]
Ps = [Variable(P.cuda(), requires_grad=True) for P in Ps]
Ns = [Variable(N.cuda(), requires_grad=True) for N in Ns]
if args.metric == "biaffine":
Ws = [Variable(W.cuda(), requires_grad=True) for W in Ws]
Vs = [Variable(V.cuda(), requires_grad=True) for V in Vs]
# Ms = [ U.mm(U.t()) for U in Us ]
say("\nEncoder: {}\n".format(encoder))
for i, classifier in enumerate(classifiers):
say("Classifier-{}: {}\n".format(i, classifier))
say("Critic: {}\n".format(critic))
requires_grad = lambda x: x.requires_grad
task_params = list(encoder.parameters())
for classifier in classifiers:
task_params += list(classifier.parameters())
task_params += list(critic.parameters())
task_params += Us
task_params += Ps
task_params += Ns
if args.metric == "biaffine":
task_params += Ws
task_params += Vs
optim_model = optim.Adam(filter(requires_grad, task_params),
lr=args.lr,
weight_decay=1e-4)
say("Training will begin from scratch\n")
best_dev = 0
best_test = 0
iter_cnt = 0
for epoch in range(args.max_epoch):
if args.metric == "biaffine":
mats = [Us, Ws, Vs]
else:
mats = [Us, Ps, Ns]
iter_cnt = train_epoch(iter_cnt, encoder, classifiers, critic, mats,
[train_loaders, unl_loader, valid_loader], args,
optim_model)
if valid_loader:
(curr_dev, oracle_curr_dev), confusion_mat = evaluate(
encoder, classifiers, mats, [train_loaders, valid_loader],
args)
say("Dev accuracy/oracle: {:.4f}/{:.4f}\n".format(
curr_dev, oracle_curr_dev))
(curr_test, oracle_curr_test), confusion_mat = evaluate(
encoder, classifiers, mats, [train_loaders, test_loader], args)
say("Test accuracy/oracle: {:.4f}/{:.4f}\n".format(
curr_test, oracle_curr_test))
if valid_loader and curr_dev >= best_dev:
best_dev = curr_dev
best_test = curr_test
print(confusion_mat)
if args.save_model:
say(
colored(
"Save model to {}\n".format(args.save_model + ".best"),
'red'))
torch.save([encoder, classifiers, Us, Ps, Ns],
args.save_model + ".best")
say("\n")
if valid_loader:
say(colored("Best test accuracy {:.4f}\n".format(best_test), 'red'))
say(
colored("Test accuracy after training {:.4f}\n".format(curr_test),
'red'))
def visualize(args):
if args.mop == 3:
encoder, classifiers, source_classifier = torch.load(args.load_model)
elif args.mop == 2:
encoder, classifiers, Us, Ps, Ns = torch.load(args.load_model)
else:
say("\nUndefined --mop\n")
return
map(lambda m: m.eval(), [encoder] + classifiers)
if args.cuda:
map(lambda m: m.cuda(), [encoder] + classifiers)
source_train_sets = args.train.split(',')
train_loaders = []
for source in source_train_sets:
filepath = os.path.join(DATA_DIR, "%s_train.svmlight" % (source))
train_dataset = AmazonDataset(filepath)
train_loader = data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0
)
train_loaders.append(train_loader)
test_filepath = os.path.join(DATA_DIR, "%s_train.svmlight" % (args.test))
test_dataset = AmazonDataset(test_filepath)
test_loader = data.DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=0
)
say("Corpus loaded.\n")
source_hs = []
source_ys = []
source_num = []
for loader in train_loaders:
encoding_vecs = torch.FloatTensor()
labels = torch.LongTensor()
if args.cuda:
encoding_vecs = encoding_vecs.cuda()
labels = labels.cuda()
for batch, label in loader:
if args.cuda:
batch = batch.cuda()
label = label.cuda()
batch = Variable(batch)
hidden = encoder(batch)
encoding_vecs = torch.cat([encoding_vecs, hidden.data])
labels = torch.cat([labels, label.view(-1, 1)])
source_hs.append(encoding_vecs)
source_ys.append(labels)
source_num.append(labels.shape[0])
ht = torch.FloatTensor()
yt = torch.LongTensor()
if args.cuda:
ht = ht.cuda()
yt = yt.cuda()
for batch, label in test_loader:
if args.cuda:
batch = batch.cuda()
label = label.cuda()
batch = Variable(batch)
hidden = encoder(batch)
ht = torch.cat([ht, hidden.data])
yt = torch.cat([yt, label.view(-1, 1)])
h_both = torch.cat(source_hs + [ht]).cpu().numpy()
y_both = torch.cat(source_ys + [yt]).cpu().numpy()
say("Dimension reduction...\n")
tsne = TSNE(perplexity=30, n_components=2, n_iter=3300)
vdata = tsne.fit_transform(h_both)
print vdata.shape, source_num
torch.save([vdata, y_both, source_num], 'vis/%s-%s-mop%d.vdata' % (args.train, args.test, args.mop))
ms_plot_embedding_sep(vdata, y_both, source_num, args.save_image)