attribute_path = base_path + attribute_name
experiment_path = utils.create_experiment_directory()
model_params = run_utils.create_model_params(experiment_path, epoch, lf, beta, int(epoch / 4), expanded_user_item, mixup,
no_generative_factors, epoch, is_hessian_penalty_activated, used_data)
args.max_epochs = epoch
wandb_logger = WandbLogger(project='recommender-xai', tags=['morpho', train_tag], name=wandb_name)
trainer = pl.Trainer.from_argparse_args(args,
logger=wandb_logger, #False
gpus=0,
weights_summary='full',
checkpoint_callback = False,
callbacks = [ProgressBar(), EarlyStopping(monitor='train_loss')]
)
if(train):
print('<---------------------------------- VAE Training ---------------------------------->')
print("Running with the following configuration: \n{}".format(args))
if (synthetic_data):
model_params['synthetic_data'], model_params['syn_y'] = data_utils.create_synthetic_data(no_generative_factors,
experiment_path,
expanded_user_item,
continous_data,
normalvariate,
noise = False)
generate_distribution_df()
model = VAE(model_params)
def test_v1_7_0_progress_bar():
with pytest.deprecated_call(match="has been deprecated in v1.5 and will be removed in v1.7."):
_ = ProgressBar()
wandb_logger = WandbLogger(project='recommender-xai',
tags=['vae', train_tag],
name=wandb_name)
trainer = pl.Trainer.from_argparse_args(
args,
# limit_test_batches=0.1,
# precision =16,
logger=wandb_logger, # False
gradient_clip_val=0.5,
# accumulate_grad_batches=0,
gpus=0,
weights_summary='full',
checkpoint_callback=False,
callbacks=[
ProgressBar(),
EarlyStopping(monitor='train_loss')
])
if (train):
print(
'<---------------------------------- VAE Training ---------------------------------->'
)
print(
"Running with the following configuration: \n{}"
.format(args))
if (synthetic_data):
model_params['synthetic_data'], model_params[
'syn_y'] = data_utils.create_synthetic_data(
no_generative_factors, experiment_path,
expanded_user_item, continous_data,