Skip to content
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

Fix float errors #842

Draft
wants to merge 2 commits into
base: main
Choose a base branch
from
Draft

Fix float errors #842

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
dcb084f
Fix float errors
Jan 24, 2022
aabc5e5
Format with black
Jan 24, 2022
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
8 changes: 6 additions & 2 deletions pytorch_forecasting/metrics.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -1103,10 +1103,14 @@ def to_quantiles(self, y_pred: torch.Tensor, quantiles: List[float] = None, n_sa
quantiles = self.quantiles
try:
distribution = self.map_x_to_distribution(y_pred)
quantiles = distribution.icdf(torch.tensor(quantiles, device=y_pred.device)[:, None, None]).permute(1, 2, 0)
quantiles = distribution.icdf(
torch.tensor(quantiles, device=y_pred.device, dtype=torch.float64)[:, None, None]
).permute(1, 2, 0)
except NotImplementedError: # resort to derive quantiles empirically
samples = torch.sort(self.sample(y_pred, n_samples), -1).values
quantiles = torch.quantile(samples, torch.tensor(quantiles, device=samples.device), dim=2).permute(1, 2, 0)
quantiles = torch.quantile(
samples, torch.tensor(quantiles, device=samples.device, dtype=torch.float64), dim=2
).permute(1, 2, 0)
return quantiles


Expand Down
2 changes: 1 addition & 1 deletion pytorch_forecasting/models/deepar/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -280,7 +280,7 @@ def decode_one(
hidden_state,
):
x = input_vector[:, [idx]]
x[:, 0, target_pos] = lagged_targets[-1]
x[:, 0, target_pos] = lagged_targets[-1].float()
for lag, lag_positions in lagged_target_positions.items():
if idx > lag:
x[:, 0, lag_positions] = lagged_targets[-lag]
Expand Down