Skip to content

Fix examples #1589

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 3 commits into
base: main
Choose a base branch
from
Open

Fix examples #1589

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
6364b25
Compatibility with newer optuna version
freesoul Aug 4, 2024
9d5eb77
Fix typo
freesoul Aug 4, 2024
83d21a7
Merge branch 'master' into pr/1589
fkiraly Aug 25, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
2 changes: 1 addition & 1 deletion examples/stallion.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -90,7 +90,7 @@


# save datasets
training.save("t raining.pkl")
training.save("training.pkl")
validation.save("validation.pkl")

early_stop_callback = EarlyStopping(monitor="val_loss", min_delta=1e-4, patience=10, verbose=False, mode="min")
Expand Down
18 changes: 9 additions & 9 deletions pytorch_forecasting/models/temporal_fusion_transformer/tuning.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -26,9 +26,13 @@
optuna_logger = logging.getLogger("optuna")


# need to inherit from callback for this to work
class PyTorchLightningPruningCallbackAdjusted(PyTorchLightningPruningCallback, pl.Callback):
pass
# On previous versions of optuna, PyTorchLightningPruningCallback did not inherit from pl.Callback.
# In newest it does, so if we try to inherit from it again, we get a TypeError due to bad use of inheritance.
if not issubclass(PyTorchLightningPruningCallback, pl.Callback):
class PyTorchLightningPruningCallbackAdjusted(PyTorchLightningPruningCallback, pl.Callback):
pass
else:
PyTorchLightningPruningCallbackAdjusted = PyTorchLightningPruningCallback


def optimize_hyperparameters(
Expand Down Expand Up @@ -109,16 +113,12 @@ def optimize_hyperparameters(
optuna_verbose = logging_level[verbose]
optuna.logging.set_verbosity(optuna_verbose)

loss = kwargs.get(
"loss", QuantileLoss()
) # need a deepcopy of loss as it will otherwise propagate from one trial to the next
loss = kwargs.get("loss", QuantileLoss()) # need a deepcopy of loss as it will otherwise propagate from one trial to the next

# create objective function
def objective(trial: optuna.Trial) -> float:
# Filenames for each trial must be made unique in order to access each checkpoint.
checkpoint_callback = ModelCheckpoint(
dirpath=os.path.join(model_path, "trial_{}".format(trial.number)), filename="{epoch}", monitor="val_loss"
)
checkpoint_callback = ModelCheckpoint(dirpath=os.path.join(model_path, "trial_{}".format(trial.number)), filename="{epoch}", monitor="val_loss")

learning_rate_callback = LearningRateMonitor()
logger = TensorBoardLogger(log_dir, name="optuna", version=trial.number)
Expand Down
Loading