def _init_svae_model(self):
self.svae = SVAE(**self.model_config['SVAE']['Parameters'],
vocab_size=self.vocab_size).to(self.device)
self.svae_optim = optim.Adam(
self.svae.parameters(),
lr=self.model_config['SVAE']['learning_rate'])
self.svae.train()
print(f'{datetime.now()}: Initialised SVAE successfully')
def _load_pretrained_model(self):
# Initialise SVAE with saved parameters. TODO: Save model hyperparameters to disk with the saved weights
self.svae = SVAE(**self.model_config['SVAE']['Parameters'], vocab_size=self.vocab_size).to(self.device)
# Loads pre-trained SVAE model from disk and modifies
svae_best_model_path = 'best models/svae.pt'
self.svae.load_state_dict(torch.load(svae_best_model_path))
print(f'{datetime.now()}: Loaded pretrained SVAE\n{self.svae}')
class Identity(nn.Module):
def __init__(self):
super(Identity, self).__init__()
def forward(self, x):
return x
self.svae.outputs2vocab=Identity() # Removes hidden2output layer
print(f'{datetime.now()}: Modified pretrained SVAE\n{self.svae}')
def _init_models(self, mode: str):
""" Initialises models, loss functions, optimisers and sets models to training mode """
# Task Learner
self.task_learner = TaskLearner(**self.model_config['TaskLearner']['Parameters'],
vocab_size=self.vocab_size,
tagset_size=self.tagset_size,
task_type=self.task_type).to(self.device)
# Loss functions
if self.task_type == 'SEQ':
self.tl_loss_fn = nn.NLLLoss().to(self.device)
if self.task_type == 'CLF':
self.tl_loss_fn = nn.CrossEntropyLoss().to(self.device)
# Optimisers
self.tl_optim = optim.SGD(self.task_learner.parameters(),
lr=self.model_config['TaskLearner']['learning_rate'])#, momentum=0, weight_decay=0.1)
# Learning rate scheduler
# Note: LR likely GT Adam
# self.tl_sched = optim.lr_scheduler.ReduceLROnPlateau(self.tl_optim, 'min', factor=0.5, patience=10)
# Training Modes
self.task_learner.train()
# SVAAL needs to initialise SVAE and DISC in addition to TL
if mode == 'svaal':
# Models
self.svae = SVAE(**self.model_config['SVAE']['Parameters'],
vocab_size=self.vocab_size).to(self.device)
self.discriminator = Discriminator(**self.model_config['Discriminator']['Parameters']).to(self.device)
# Loss Function (SVAE defined within its class)
self.dsc_loss_fn = nn.BCELoss().to(self.device)
# Optimisers
# Note: Adam will likely have a lower lr than SGD
self.svae_optim = optim.Adam(self.svae.parameters(),
lr=self.model_config['SVAE']['learning_rate'])
self.dsc_optim = optim.Adam(self.discriminator.parameters(),
lr=self.model_config['Discriminator']['learning_rate'])
# Training Modes
self.svae.train()
self.discriminator.train()
print(f'{datetime.now()}: Models initialised successfully')
def main(args):
def interpolate(start, end, steps):
interpolation = np.zeros((start.shape[0], steps + 2))
for dim, (s, e) in enumerate(zip(start, end)):
interpolation[dim] = np.linspace(s, e, steps + 2)
return interpolation.T
def idx2word(sent_list, i2w, pad_idx):
sent = []
for s in sent_list:
sent.append(" ".join([i2w[str(int(idx))] \
for idx in s if int(idx) is not pad_idx]))
return sent
with open(args.data_dir + '/vocab.json', 'r') as file:
vocab = json.load(file)
w2i, i2w = vocab['w2i'], vocab['i2w']
#Load model
model = SVAE(
vocab_size=len(w2i),
embed_dim=args.embedding_dimension,
hidden_dim=args.hidden_dimension,
latent_dim=args.latent_dimension,
teacher_forcing=False,
dropout=args.dropout,
n_direction=(2 if args.bidirectional else 1),
n_parallel=args.n_layer,
max_src_len=args.max_src_length, #influence in inference stage
max_tgt_len=args.max_tgt_length,
sos_idx=w2i['<sos>'],
eos_idx=w2i['<eos>'],
pad_idx=w2i['<pad>'],
unk_idx=w2i['<unk>'],
)
path = os.path.join('checkpoint', args.load_checkpoint)
if not os.path.exists(path):
raise FileNotFoundError(path)
model.load_state_dict(torch.load(path))
print("Model loaded from %s" % (path))
if torch.cuda.is_available():
model = model.cuda()
model.eval()
samples, z = model.inference(n=args.num_samples)
print('----------SAMPLES----------')
print(*idx2word(sent_list=samples, i2w=i2w, pad_idx=w2i['<pad>']),
sep='\n')
z1 = torch.randn([args.latent_dimension]).numpy()
z2 = torch.randn([args.latent_dimension]).numpy()
z = torch.from_numpy(interpolate(start=z1, end=z2, steps=8)).float()
samples, _ = model.inference(z=z)
print('-------INTERPOLATION-------')
print(*idx2word(sent_list=samples, i2w=i2w, pad_idx=w2i['<pad>']),
sep='\n')
def main(args):
splits = ['train', 'valid'] + (['dev'] if args.test else [])
print(args)
#Load dataset
datasets = OrderedDict()
for split in splits:
datasets[split] = seq_data(data_dir=args.data_dir,
split=split,
mt=args.mt,
create_data=args.create_data,
max_src_len=args.max_src_length,
max_tgt_len=args.max_tgt_length,
min_occ=args.min_occ)
print('Data OK')
#Load model
model = SVAE(
vocab_size=datasets['train'].vocab_size,
embed_dim=args.embedding_dimension,
hidden_dim=args.hidden_dimension,
latent_dim=args.latent_dimension,
#word_drop=args.word_dropout,
teacher_forcing=args.teacher_forcing,
dropout=args.dropout,
n_direction=args.bidirectional,
n_parallel=args.n_layer,
attn=args.attention,
max_src_len=args.max_src_length, #influence in inference stage
max_tgt_len=args.max_tgt_length,
sos_idx=datasets['train'].sos_idx,
eos_idx=datasets['train'].eos_idx,
pad_idx=datasets['train'].pad_idx,
unk_idx=datasets['train'].unk_idx)
if args.fasttext:
prt = torch.load(args.data_dir + '/prt_fasttext.model')
model.load_prt(prt)
print('Model OK')
if torch.cuda.is_available():
model = model.cuda()
device = model.device
#Training phase with validation(earlystopping)
tracker = Tracker(patience=10,
verbose=True) #record training history & es function
optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
step = 0
for epoch in range(args.epochs):
for split in splits:
data_loader = DataLoader(dataset=datasets[split],
batch_size=args.n_batch,
shuffle=(split == 'train'),
num_workers=cpu_count(),
pin_memory=torch.cuda.is_available())
if split == 'train':
model.train()
else:
model.eval()
#Executing
for i, data in enumerate(data_loader):
src, srclen, tgt, tgtlen = \
data['src'], data['srclen'], data['tgt'], data['tgtlen']
#FP
logits, (mu, logv,
z), generations = model(src, srclen, tgt, tgtlen,
split)
#FP for groundtruth
#h_pred, h_tgt = model.forward_gt(generations, tgt, tgtlen)
#LOSS(weighted)
NLL, KL, KL_W = model.loss(logits, tgt.to(device),
data['tgtlen'], mu, logv, step,
args.k, args.x0, args.af)
#GLOBAL = model.global_loss(h_pred, h_tgt)
GLOBAL = 0
loss = (NLL + KL * KL_W + GLOBAL) / data['src'].size(0)
#BP & OPTIM
if split == 'train':
optimizer.zero_grad()
loss.backward()
optimizer.step()
step += 1
#RECORD & RESULT(batch)
if i % 50 == 0 or i + 1 == len(data_loader):
#NLL.data = torch.cuda.FloatTensor([NLL.data])
#KL.data = torch.cuda.FloatTensor([KL.data])
print(
"{} Phase - Batch {}/{}, Loss: {}, NLL: {}, KL: {}, KL-W: {}, G: {}"
.format(split.upper(), i,
len(data_loader) - 1, loss, NLL, KL, KL_W,
GLOBAL))
tracker._elbo(torch.Tensor([loss]))
if split == 'valid':
tracker.record(tgt, generations, datasets['train'].i2w,
datasets['train'].pad_idx,
datasets['train'].eos_idx,
datasets['train'].unk_idx, z)
#SAVING & RESULT(epoch)
if split == 'valid':
tracker.dumps(epoch, args.dump_file) #dump the predicted text.
else:
tracker._save_checkpoint(
epoch, args.model_file,
model.state_dict()) #save the checkpooint
print("{} Phase - Epoch {} , Mean ELBO: {}".format(
split.upper(), epoch, torch.mean(tracker.elbo)))
tracker._purge()
def train_single_cycle(self, parameterisation=None):
""" Performs a single cycle of the active learning routine at a specified data split for optimisation purposes"""
print(parameterisation)
if parameterisation is None:
params = {
'tl': self.model_config['TaskLearner']['Parameters'],
'svae': self.model_config['SVAE']['Parameters'],
'disc': self.model_config['Discriminator']['Parameters']
}
params.update({
'tl_learning_rate':
self.model_config['TaskLearner']['learning_rate']
})
params.update({
'svae_learning_rate':
self.model_config['SVAE']['learning_rate']
})
params.update({
'disc_learning_rate':
self.model_config['Discriminator']['learning_rate']
})
params.update({'epochs': self.config['Train']['epochs']})
params.update({'k': self.model_config['SVAE']['k']})
params.update({'x0': self.model_config['SVAE']['x0']})
params.update({
'adv_hyperparameter':
self.model_config['SVAE']['adversarial_hyperparameter']
})
if parameterisation:
params = {
'epochs':
parameterisation['epochs'],
'batch_size':
parameterisation['batch_size'],
'tl_learning_rate':
self.model_config['TaskLearner']
['learning_rate'], #parameterisation['tl_learning_rate'],
'svae_learning_rate':
parameterisation['svae_learning_rate'],
'disc_learning_rate':
parameterisation['disc_learning_rate'],
'k':
parameterisation['svae_k'],
'x0':
parameterisation['svae_x0'],
'adv_hyperparameter':
self.model_config['SVAE']
['adversarial_hyperparameter'], #parameterisation['svae_adv_hyperparameter'],
'tl':
self.model_config['TaskLearner']['Parameters'],
#{'embedding_dim': parameterisation['tl_embedding_dim'],
# 'hidden_dim': parameterisation['tl_hidden_dim'],
# 'rnn_type': parameterisation['tl_rnn_type']},
'svae': {
'embedding_dim':
parameterisation['svae_embedding_dim'],
'hidden_dim':
parameterisation['svae_hidden_dim'],
'word_dropout':
parameterisation['svae_word_dropout'],
'embedding_dropout':
parameterisation['svae_embedding_dropout'],
'num_layers':
self.model_config['SVAE']['Parameters']
['num_layers'], #parameterisation['svae_num_layers'],
'bidirectional':
self.model_config['SVAE']['Parameters']
['bidirectional'], #parameterisation['svae_bidirectional'],
'rnn_type':
self.model_config['SVAE']['Parameters']
['rnn_type'], #parameterisation['svae_rnn_type'],
'latent_size':
parameterisation['latent_size']
},
'disc': {
'z_dim': parameterisation['latent_size'],
'fc_dim': parameterisation['disc_fc_dim']
}
}
split = self.config['Train']['cycle_frac']
print(f'\n{datetime.now()}\nSplit: {split*100:0.0f}%')
meta = f' {self.al_mode} run x data split {split*100:0.0f}'
self._init_dataset()
train_dataset = self.datasets['train']
dataset_size = len(train_dataset)
self.budget = math.ceil(
self.budget_frac *
dataset_size) # currently can only have a fixed budget size
Sampler.__init__(self, self.budget)
all_indices = set(
np.arange(dataset_size)) # indices of all samples in train
k_initial = math.ceil(
len(all_indices) *
split) # number of initial samples given split size
initial_indices = random.sample(
list(all_indices),
k=k_initial) # random sample of initial indices from train
sampler_init = data.sampler.SubsetRandomSampler(
initial_indices
) # sampler method for dataloader to randomly sample initial indices
current_indices = list(
initial_indices) # current set of labelled indices
dataloader_l = data.DataLoader(train_dataset,
sampler=sampler_init,
batch_size=params['batch_size'],
drop_last=True)
dataloader_v = data.DataLoader(self.datasets['valid'],
batch_size=params['batch_size'],
shuffle=True,
drop_last=False)
dataloader_t = data.DataLoader(self.datasets['test'],
batch_size=params['batch_size'],
shuffle=True,
drop_last=False)
unlabelled_indices = np.setdiff1d(
list(all_indices), current_indices
) # set of unlabelled indices (all - initial/current)
unlabelled_sampler = data.sampler.SubsetRandomSampler(
unlabelled_indices
) # sampler method for dataloader to randomly sample unlabelled indices
dataloader_u = data.DataLoader(self.datasets['train'],
sampler=unlabelled_sampler,
batch_size=params['batch_size'],
drop_last=False)
print(
f'{datetime.now()}: Indices - Labelled {len(current_indices)} Unlabelled {len(unlabelled_indices)} Total {len(all_indices)}'
)
# Initialise models
task_learner = TaskLearner(**params['tl'],
vocab_size=self.vocab_size,
tagset_size=self.tagset_size,
task_type=self.task_type).to(self.device)
if self.task_type == 'SEQ':
tl_loss_fn = nn.NLLLoss().to(self.device)
if self.task_type == 'CLF':
tl_loss_fn = nn.CrossEntropyLoss().to(self.device)
tl_optim = optim.SGD(
task_learner.parameters(),
lr=params['tl_learning_rate']) #, momentum=0, weight_decay=0.1)
task_learner.train()
svae = SVAE(**params['svae'],
vocab_size=self.vocab_size).to(self.device)
discriminator = Discriminator(**params['disc']).to(self.device)
dsc_loss_fn = nn.BCELoss().to(self.device)
svae_optim = optim.Adam(svae.parameters(),
lr=params['svae_learning_rate'])
dsc_optim = optim.Adam(discriminator.parameters(),
lr=params['disc_learning_rate'])
# Training Modes
svae.train()
discriminator.train()
print(f'{datetime.now()}: Models initialised successfully')
# Perform AL training and sampling
early_stopping = EarlyStopping(
patience=self.config['Train']['es_patience'],
verbose=True,
path="checkpoints/checkpoint.pt")
dataset_size = len(dataloader_l) + len(
dataloader_u) if dataloader_u is not None else len(dataloader_l)
train_iterations = dataset_size * (params['epochs'] + 1)
print(
f'{datetime.now()}: Dataset size (batches) {dataset_size} Training iterations (batches) {train_iterations}'
)
step = 0
epoch = 1
for train_iter in tqdm(range(train_iterations),
desc='Training iteration'):
batch_sequences_l, batch_lengths_l, batch_tags_l = next(
iter(dataloader_l))
if torch.cuda.is_available():
batch_sequences_l = batch_sequences_l.to(self.device)
batch_lengths_l = batch_lengths_l.to(self.device)
batch_tags_l = batch_tags_l.to(self.device)
if dataloader_u is not None:
batch_sequences_u, batch_lengths_u, _ = next(
iter(dataloader_u))
batch_sequences_u = batch_sequences_u.to(self.device)
batch_length_u = batch_lengths_u.to(self.device)
# Strip off tag padding and flatten
# Don't do sequences here as its done in the forward pass of the seq2seq models
batch_tags_l = trim_padded_seqs(batch_lengths=batch_lengths_l,
batch_sequences=batch_tags_l,
pad_idx=self.pad_idx).view(-1)
# Task Learner Step
tl_optim.zero_grad()
tl_preds = task_learner(batch_sequences_l, batch_lengths_l)
tl_loss = tl_loss_fn(tl_preds, batch_tags_l)
tl_loss.backward()
tl_optim.step()
# Used in SVAE and Discriminator
batch_size_l = batch_sequences_l.size(0)
batch_size_u = batch_sequences_u.size(0)
# SVAE Step
for i in range(self.svae_iterations):
logp_l, mean_l, logv_l, z_l = svae(batch_sequences_l,
batch_lengths_l)
NLL_loss_l, KL_loss_l, KL_weight_l = svae.loss_fn(
logp=logp_l,
target=batch_sequences_l,
length=batch_lengths_l,
mean=mean_l,
logv=logv_l,
anneal_fn=self.model_config['SVAE']['anneal_function'],
step=step,
k=params['k'],
x0=params['x0'])
logp_u, mean_u, logv_u, z_u = svae(batch_sequences_u,
batch_lengths_u)
NLL_loss_u, KL_loss_u, KL_weight_u = svae.loss_fn(
logp=logp_u,
target=batch_sequences_u,
length=batch_lengths_u,
mean=mean_u,
logv=logv_u,
anneal_fn=self.model_config['SVAE']['anneal_function'],
step=step,
k=params['k'],
x0=params['x0'])
# VAE loss
svae_loss_l = (NLL_loss_l +
KL_weight_l * KL_loss_l) / batch_size_l
svae_loss_u = (NLL_loss_u +
KL_weight_u * KL_loss_u) / batch_size_u
# Adversary loss - trying to fool the discriminator!
dsc_preds_l = discriminator(z_l) # mean_l
dsc_preds_u = discriminator(z_u) # mean_u
dsc_real_l = torch.ones(batch_size_l)
dsc_real_u = torch.ones(batch_size_u)
if torch.cuda.is_available():
dsc_real_l = dsc_real_l.to(self.device)
dsc_real_u = dsc_real_u.to(self.device)
adv_dsc_loss_l = dsc_loss_fn(dsc_preds_l, dsc_real_l)
adv_dsc_loss_u = dsc_loss_fn(dsc_preds_u, dsc_real_u)
adv_dsc_loss = adv_dsc_loss_l + adv_dsc_loss_u
total_svae_loss = svae_loss_u + svae_loss_l + params[
'adv_hyperparameter'] * adv_dsc_loss
svae_optim.zero_grad()
total_svae_loss.backward()
svae_optim.step()
if i < self.svae_iterations - 1:
batch_sequences_l, batch_lengths_l, _ = next(
iter(dataloader_l))
batch_sequences_u, batch_length_u, _ = next(
iter(dataloader_u))
if torch.cuda.is_available():
batch_sequences_l = batch_sequences_l.to(self.device)
batch_lengths_l = batch_lengths_l.to(self.device)
batch_sequences_u = batch_sequences_u.to(self.device)
batch_length_u = batch_length_u.to(self.device)
step += 1
# Discriminator Step
for j in range(self.dsc_iterations):
with torch.no_grad():
_, _, _, z_l = svae(batch_sequences_l, batch_lengths_l)
_, _, _, z_u = svae(batch_sequences_u, batch_lengths_u)
dsc_preds_l = discriminator(z_l)
dsc_preds_u = discriminator(z_u)
dsc_real_l = torch.ones(batch_size_l)
dsc_real_u = torch.zeros(batch_size_u)
if torch.cuda.is_available():
dsc_real_l = dsc_real_l.to(self.device)
dsc_real_u = dsc_real_u.to(self.device)
# Discriminator wants to minimise the loss here
dsc_loss_l = dsc_loss_fn(dsc_preds_l, dsc_real_l)
dsc_loss_u = dsc_loss_fn(dsc_preds_u, dsc_real_u)
total_dsc_loss = dsc_loss_l + dsc_loss_u
dsc_optim.zero_grad()
total_dsc_loss.backward()
dsc_optim.step()
# Sample new batch of data while training adversarial network
if j < self.dsc_iterations - 1:
batch_sequences_l, batch_lengths_l, _ = next(
iter(dataloader_l))
batch_sequences_u, batch_length_u, _ = next(
iter(dataloader_u))
if torch.cuda.is_available():
batch_sequences_l = batch_sequences_l.to(self.device)
batch_lengths_l = batch_lengths_l.to(self.device)
batch_sequences_u = batch_sequences_u.to(self.device)
batch_length_u = batch_length_u.to(self.device)
if (train_iter % dataset_size == 0):
print("Initiating Early Stopping")
early_stopping(
tl_loss, task_learner
) # TODO: Review. Should this be the metric we early stop on?
if early_stopping.early_stop:
print(
f'Early stopping at {train_iter}/{train_iterations} training iterations'
)
break
if (train_iter > 0) & (epoch == 1
or train_iter % dataset_size == 0):
if train_iter % dataset_size == 0:
val_metrics = self.evaluation(task_learner=task_learner,
dataloader=dataloader_v,
task_type=self.task_type)
val_string = f'Task Learner ({self.task_type}) Validation ' + f'Scores:\nF1: Macro {val_metrics["f1 macro"]*100:0.2f}% Micro {val_metrics["f1 micro"]*100:0.2f}%\n' if self.task_type == 'SEQ' else f'Accuracy {val_metrics["accuracy"]*100:0.2f}'
print(val_string)
if (train_iter > 0) & (train_iter % dataset_size == 0):
# Completed an epoch
train_iter_str = f'Train Iter {train_iter} - Losses (TL-{self.task_type} {tl_loss:0.2f} | SVAE {total_svae_loss:0.2f} | Disc {total_dsc_loss:0.2f} | Learning rates: TL ({tl_optim.param_groups[0]["lr"]})'
print(train_iter_str)
print(f'Completed epoch: {epoch}')
epoch += 1
# Evaluation at the end of the first training cycle
test_metrics = self.evaluation(task_learner=task_learner,
dataloader=dataloader_t,
task_type='SEQ')
f1_macro_1 = test_metrics['f1 macro']
# SVAE and Discriminator need to be evaluated on the TL metric n+1 split from their current training split
# So, data needs to be sampled via SVAAL and then the TL retrained. The final metric from the retrained TL is then used to
# optimise the SVAE and Discriminator. For this optimisation problem, the TL parameters are fixed.
# Sample data via SVAE and Discriminator
sampled_indices, _, _ = self.sample_adversarial(
svae=svae,
discriminator=discriminator,
data=dataloader_u,
indices=unlabelled_indices,
cuda=True) # TODO: review usage of indices arg
# Add new samples to labelled dataset
current_indices = list(current_indices) + list(sampled_indices)
sampler = data.sampler.SubsetRandomSampler(current_indices)
self.labelled_dataloader = data.DataLoader(self.datasets['train'],
sampler=sampler,
batch_size=self.batch_size,
drop_last=True)
dataloader_l = self.labelled_dataloader # to maintain naming conventions
print(
f'{datetime.now()}: Indices - Labelled {len(current_indices)} Unlabelled {len(unlabelled_indices)} Total {len(all_indices)}'
)
task_learner = TaskLearner(**params['tl'],
vocab_size=self.vocab_size,
tagset_size=self.tagset_size,
task_type=self.task_type).to(self.device)
if self.task_type == 'SEQ':
tl_loss_fn = nn.NLLLoss().to(self.device)
if self.task_type == 'CLF':
tl_loss_fn = nn.CrossEntropyLoss().to(self.device)
tl_optim = optim.SGD(
task_learner.parameters(),
lr=params['tl_learning_rate']) #, momentum=0, weight_decay=0.1)
task_learner.train()
early_stopping = EarlyStopping(
patience=self.config['Train']['es_patience'],
verbose=True,
path="checkpoints/checkpoint.pt")
print(f'{datetime.now()}: Task Learner initialised successfully')
# Train Task Learner on adversarially selected samples
dataset_size = len(dataloader_l)
train_iterations = dataset_size * (params['epochs'] + 1)
epoch = 1
for train_iter in tqdm(range(train_iterations),
desc='Training iteration'):
batch_sequences_l, batch_lengths_l, batch_tags_l = next(
iter(dataloader_l))
if torch.cuda.is_available():
batch_sequences_l = batch_sequences_l.to(self.device)
batch_lengths_l = batch_lengths_l.to(self.device)
batch_tags_l = batch_tags_l.to(self.device)
# Strip off tag padding and flatten
# Don't do sequences here as its done in the forward pass of the seq2seq models
batch_tags_l = trim_padded_seqs(batch_lengths=batch_lengths_l,
batch_sequences=batch_tags_l,
pad_idx=self.pad_idx).view(-1)
# Task Learner Step
tl_optim.zero_grad()
tl_preds = task_learner(batch_sequences_l, batch_lengths_l)
tl_loss = tl_loss_fn(tl_preds, batch_tags_l)
tl_loss.backward()
tl_optim.step()
if (train_iter % dataset_size == 0):
print("Initiating Early Stopping")
early_stopping(
tl_loss, task_learner
) # TODO: Review. Should this be the metric we early stop on?
if early_stopping.early_stop:
print(
f'Early stopping at {train_iter}/{train_iterations} training iterations'
)
break
if (train_iter > 0) & (epoch == 1
or train_iter % dataset_size == 0):
if train_iter % dataset_size == 0:
val_metrics = self.evaluation(task_learner=task_learner,
dataloader=dataloader_v,
task_type=self.task_type)
val_string = f'Task Learner ({self.task_type}) Validation ' + f'Scores:\nF1: Macro {val_metrics["f1 macro"]*100:0.2f}% Micro {val_metrics["f1 micro"]*100:0.2f}%\n' if self.task_type == 'SEQ' else f'Accuracy {val_metrics["accuracy"]*100:0.2f}'
print(val_string)
if (train_iter > 0) & (train_iter % dataset_size == 0):
# Completed an epoch
train_iter_str = f'Train Iter {train_iter} - Losses (TL-{self.task_type} {tl_loss:0.2f} | Learning rate: TL ({tl_optim.param_groups[0]["lr"]})'
print(train_iter_str)
print(f'Completed epoch: {epoch}')
epoch += 1
# Compute test metrics
test_metrics = self.evaluation(task_learner=task_learner,
dataloader=dataloader_t,
task_type='SEQ')
print(
f'{datetime.now()}: Test Eval.: F1 Scores - Macro {test_metrics["f1 macro"]*100:0.2f}% Micro {test_metrics["f1 micro"]*100:0.2f}%'
)
# return test_metrics["f1 macro"]
# Should the output be maximum rate of the change between iter n and iter n+1 metrics? this makes more sense than just f1 macro?
f1_macro_diff = test_metrics['f1 macro'] - f1_macro_1
print(f'Macro f1 difference: {f1_macro_diff*100:0.2f}%')
return f1_macro_diff