def test_integration(dataloaders_fixed_window_without_covariates, tmp_path,
gpus):
train_dataloader = dataloaders_fixed_window_without_covariates["train"]
val_dataloader = dataloaders_fixed_window_without_covariates["val"]
test_dataloader = dataloaders_fixed_window_without_covariates["test"]
early_stop_callback = EarlyStopping(monitor="val_loss",
min_delta=1e-4,
patience=1,
verbose=False,
mode="min")
logger = TensorBoardLogger(tmp_path)
trainer = pl.Trainer(
max_epochs=2,
gpus=gpus,
gradient_clip_val=0.1,
callbacks=[early_stop_callback],
enable_checkpointing=True,
default_root_dir=tmp_path,
limit_train_batches=2,
limit_val_batches=2,
limit_test_batches=2,
logger=logger,
)
net = NBeats.from_dataset(
train_dataloader.dataset,
learning_rate=0.15,
log_gradient_flow=True,
widths=[4, 4, 4],
log_interval=1000,
backcast_loss_ratio=1.0,
)
net.size()
try:
trainer.fit(
net,
train_dataloaders=train_dataloader,
val_dataloaders=val_dataloader,
)
test_outputs = trainer.test(net, dataloaders=test_dataloader)
assert len(test_outputs) > 0
# check loading
net = NBeats.load_from_checkpoint(
trainer.checkpoint_callback.best_model_path)
# check prediction
net.predict(val_dataloader,
fast_dev_run=True,
return_index=True,
return_decoder_lengths=True)
finally:
shutil.rmtree(tmp_path, ignore_errors=True)
net.predict(val_dataloader,
fast_dev_run=True,
return_index=True,
return_decoder_lengths=True)
def test_integration(dataloaders_fixed_window_without_coveratiates, tmp_path,
gpus):
train_dataloader = dataloaders_fixed_window_without_coveratiates["train"]
val_dataloader = dataloaders_fixed_window_without_coveratiates["val"]
early_stop_callback = EarlyStopping(monitor="val_loss",
min_delta=1e-4,
patience=1,
verbose=False,
mode="min")
logger = TensorBoardLogger(tmp_path)
checkpoint = ModelCheckpoint(filepath=tmp_path)
trainer = pl.Trainer(
checkpoint_callback=checkpoint,
max_epochs=3,
gpus=gpus,
weights_summary="top",
gradient_clip_val=0.1,
callbacks=[early_stop_callback],
fast_dev_run=True,
logger=logger,
)
net = NBeats.from_dataset(
train_dataloader.dataset,
learning_rate=0.15,
log_gradient_flow=True,
widths=[4, 4, 4],
log_interval=1000,
backcast_loss_ratio=1.0,
)
net.size()
try:
trainer.fit(
net,
train_dataloader=train_dataloader,
val_dataloaders=val_dataloader,
)
# check loading
fname = f"{trainer.checkpoint_callback.dirpath}/epoch=0.ckpt"
net = NBeats.load_from_checkpoint(fname)
# check prediction
net.predict(val_dataloader,
fast_dev_run=True,
return_index=True,
return_decoder_lengths=True)
finally:
shutil.rmtree(tmp_path, ignore_errors=True)
net.predict(val_dataloader,
fast_dev_run=True,
return_index=True,
return_decoder_lengths=True)
def model(dataloaders_fixed_window_without_covariates):
dataset = dataloaders_fixed_window_without_covariates["train"].dataset
net = NBeats.from_dataset(
dataset,
learning_rate=0.15,
log_gradient_flow=True,
widths=[4, 4, 4],
log_interval=1000,
backcast_loss_ratio=1.0,
)
return net