model = TAM(config["model"], window_length=config["model"]["context_window"])
model.to(opts.device)
weights = weights.to(opts.device)
pos_weight = weights[1] / weights[0]
pos_weight.fill_(4)
loss_function = nn.BCEWithLogitsLoss(reduction='none', pos_weight=pos_weight)
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adam(parameters)
total_params = sum(p.numel() for p in model.parameters())
total_trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Total Params:", number_h(total_params))
print("Total Trainable Params:", number_h(total_trainable_params))
####################################################################
# Training Pipeline
####################################################################
trainer = BaselineTrainer(val_loader, model, train_loader, dev_loader,
loss_function, [optimizer], config, opts.device)
####################################################################
# Training Loop
####################################################################
best_loss = None
best_score = 0
final_posteriors = []
def clf_features_baseline_runner(yaml,
word2idx,
idx2word,
weights,
cluster=False):
if cluster is False:
from logger.experiment import Experiment
# torch.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
opts, config = train_options(yaml)
####################################################################
# Data Loading and Preprocessing
####################################################################
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(
config["data"]["dataset"])
# load word embeddings
# if config["data"]["embeddings"] == "wiki.en.vec":
# word2idx, idx2word, weights = load_word_vectors_from_fasttext(
# os.path.join(EMB_DIR, config["data"]["embeddings"]),
# config["data"]["embeddings_dim"])
# else:
# word2idx, idx2word, weights = load_word_vectors(
# os.path.join(EMB_DIR, config["data"]["embeddings"]),
# config["data"]["embeddings_dim"])
####################################################################
# Linguistic Features Loading and Selection
####################################################################
# Any features/lexicon should be in the form of a dictionary
# For example: lex = {'word':[0., 1., ..., 0.]}
# load affect features
print("Loading linguistic features...")
# todo: streamline feature loading pipeline
features, feat_length = load_features(config["data"]["features"])
# assert ... same len
# build dataset
print("Building training dataset...")
train_set = ClfDataset(X_train,
y_train,
word2idx,
feat_length=feat_length,
features_dict=features)
print("Building validation dataset...")
val_set = ClfDataset(X_test,
y_test,
word2idx,
features_dict=features,
feat_length=feat_length)
test_set = ClfDataset(X_test,
y_test,
word2idx,
features_dict=features,
feat_length=feat_length)
train_set.truncate(500)
val_set.truncate(100)
src_lengths = [len(x) for x in train_set.data]
val_lengths = [len(x) for x in val_set.data]
test_lengths = [len(x) for x in test_set.data]
# select sampler & dataloader
train_sampler = BucketBatchSampler(src_lengths, config["batch_size"], True)
val_sampler = SortedSampler(val_lengths)
val_sampler_train = SortedSampler(src_lengths)
test_sampler = SortedSampler(test_lengths)
train_loader = DataLoader(train_set,
batch_sampler=train_sampler,
num_workers=opts.cores,
collate_fn=ClfCollate_withFeatures())
val_loader = DataLoader(val_set,
sampler=val_sampler,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate_withFeatures())
val_loader_train_dataset = DataLoader(train_set,
sampler=val_sampler_train,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate_withFeatures())
test_loader = DataLoader(test_set,
sampler=test_sampler,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate_withFeatures())
####################################################################
# Model
####################################################################
model = BaselineConcClassifier(ntokens=weights.shape[0],
nclasses=len(set(train_set.labels)),
feat_size=feat_length,
**config["model"])
model.word_embedding.embedding.weight = nn.Parameter(
torch.from_numpy(weights), requires_grad=False)
model.to(opts.device)
print(model)
####################################################################
# Count total parameters of model
####################################################################
total_params = sum(p.numel() for p in model.parameters())
total_trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Total Params:", number_h(total_params))
print("Total Trainable Params:", number_h(total_trainable_params))
if config["class_weights"]:
class_weights = class_weigths(train_set.labels, to_pytorch=True)
class_weights = class_weights.to(opts.device)
criterion = nn.CrossEntropyLoss(weight=class_weights)
else:
criterion = nn.CrossEntropyLoss()
clf_parameters = filter(lambda p: p.requires_grad, model.parameters())
clf_optimizer = Adam(clf_parameters, weight_decay=1e-5)
####################################################################
# Training Pipeline
####################################################################
_outputs = []
def batch_callback(i_batch, losses, batch_outputs):
_outputs.append(batch_outputs)
if trainer.step % config["log_interval"] == 0:
outputs = list(zip(*_outputs))
_losses = numpy.array(losses[-config["log_interval"]:]).mean(0)
exp.update_metric("clf-loss", _losses)
_y_hat = torch.cat(outputs[0]).max(-1)[1].cpu().data.numpy()
_y = torch.cat(outputs[1]).cpu().data.numpy()
f1 = f1_score(_y, _y_hat, average='macro')
exp.update_metric("f1-train", f1)
losses_log = exp.log_metrics(["clf-loss", 'f1-train'])
exp.update_value("progress",
trainer.progress_log + "\n" + losses_log)
# clean lines and move cursor back up N lines
print("\n\033[K" + losses_log)
print("\033[F" * (len(losses_log.split("\n")) + 2))
_outputs.clear()
# Trainer: responsible for managing the training process
trainer = ClfTrainer_withFeatures(
model=model,
train_loader=train_loader,
valid_loader=val_loader,
valid_loader_train_set=val_loader_train_dataset,
test_loader=test_loader,
criterion=criterion,
optimizers=clf_optimizer,
config=config,
device=opts.device,
batch_end_callbacks=None)
####################################################################
# Experiment: logging and visualizing the training process
####################################################################
if cluster is False:
exp = Experiment(opts.name,
config,
src_dirs=opts.source,
output_dir=EXP_DIR)
exp.add_metric("ep_loss", "line", "epoch loss", ["TRAIN", "VAL"])
exp.add_metric("ep_f1", "line", "epoch f1", ["TRAIN", "VAL"])
exp.add_metric("ep_acc", "line", "epoch accuracy", ["TRAIN", "VAL"])
exp.add_metric("ep_pre", "line", "epoch precision", ["TRAIN", "VAL"])
exp.add_metric("ep_rec", "line", "epoch recall", ["TRAIN", "VAL"])
exp.add_value("epoch", title="epoch summary", vis_type="text")
exp.add_value("progress", title="training progress", vis_type="text")
####################################################################
# Training Loop
####################################################################
best_loss = None
early_stopping = Early_stopping("min", config["patience"])
for epoch in range(config["epochs"]):
train_loss = trainer.train_epoch()
val_loss, y, y_pred = trainer.eval_epoch(val_set=True)
_, y_train, y_pred_train = trainer.eval_epoch(train_set=True)
# Calculate accuracy and f1-macro on the evaluation set
if cluster is False:
exp.update_metric("ep_loss", train_loss.item(), "TRAIN")
exp.update_metric("ep_loss", val_loss.item(), "VAL")
exp.update_metric("ep_f1", f1_macro(y_train, y_pred_train),
"TRAIN")
exp.update_metric("ep_f1", f1_macro(y, y_pred), "VAL")
exp.update_metric("ep_acc", acc(y_train, y_pred_train), "TRAIN")
exp.update_metric("ep_acc", acc(y, y_pred), "VAL")
exp.update_metric("ep_pre", precision_macro(y_train, y_pred_train),
"TRAIN")
exp.update_metric("ep_pre", precision_macro(y, y_pred), "VAL")
exp.update_metric("ep_rec", recall_macro(y_train, y_pred_train),
"TRAIN")
exp.update_metric("ep_rec", recall_macro(y, y_pred), "VAL")
print()
epoch_log = exp.log_metrics(
["ep_loss", "ep_f1", "ep_acc", "ep_pre", "ep_rec"])
print(epoch_log)
exp.update_value("epoch", epoch_log)
exp.save()
else:
print("epoch: {}, train loss: {}, val loss: {}, f1: {}".format(
epoch, train_loss.item(), val_loss.item(), f1_macro(y,
y_pred)))
# Save the model if the validation loss is the best we've seen so far.
if not best_loss or val_loss < best_loss:
best_loss = val_loss
trainer.best_val_loss = best_loss
trainer.acc = acc(y, y_pred)
trainer.f1 = f1_macro(y, y_pred)
trainer.precision = precision_macro(y, y_pred)
trainer.recall = recall_macro(y, y_pred)
trainer.checkpoint(name=opts.name, verbose=False)
if early_stopping.stop(val_loss):
print("Early Stopping...")
break
print("\n")
# return trainer.best_val_loss, trainer.acc, trainer.f1, trainer.precision, trainer.recall
#################
# Test
#################
_, y_test_, y_test_predicted = trainer.eval_epoch(test_set=True)
f1_test = f1_macro(y_test_, y_test_predicted)
acc_test = acc(y_test_, y_test_predicted)
print("#" * 33)
print("F1 for test set: {}".format(f1_test))
print("Accuracy for test set: {}".format(acc_test))
print("#" * 33)
return trainer.best_val_loss, trainer.acc, trainer.f1, trainer.precision, trainer.recall, f1_test, acc_test