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Update nbeats/sub_modules.py to remove UserWarning #1580

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Update nbeats/sub_modules.py to remove UserWarning #1580

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ab439a7
Update sub_modules.py to remove UserWarning
d-schmitt Dec 8, 2022
6d0eee6
Update sub_modules.py
d-schmitt Jun 10, 2024
3b39dbd
Update sub_modules.py
d-schmitt Jun 10, 2024
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25 changes: 14 additions & 11 deletions pytorch_forecasting/models/nbeats/sub_modules.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,19 +99,19 @@ def __init__(
backcast_linspace, forecast_linspace = linspace(backcast_length, forecast_length, centered=False)

p1, p2 = (thetas_dim // 2, thetas_dim // 2) if thetas_dim % 2 == 0 else (thetas_dim // 2, thetas_dim // 2 + 1)
s1_b = torch.tensor(
[np.cos(2 * np.pi * i * backcast_linspace) for i in self.get_frequencies(p1)], dtype=torch.float32
s1_b = torch.from_numpy(np.array(
[np.cos(2 * np.pi * i * backcast_linspace) for i in self.get_frequencies(p1)], dtype=np.float32),
) # H/2-1
s2_b = torch.tensor(
[np.sin(2 * np.pi * i * backcast_linspace) for i in self.get_frequencies(p2)], dtype=torch.float32
s2_b = torch.from_numpy(np.array(
[np.sin(2 * np.pi * i * backcast_linspace) for i in self.get_frequencies(p2)], dtype=np.float32),
)
self.register_buffer("S_backcast", torch.cat([s1_b, s2_b]))

s1_f = torch.tensor(
[np.cos(2 * np.pi * i * forecast_linspace) for i in self.get_frequencies(p1)], dtype=torch.float32
s1_f = torch.from_numpy(np.array(
[np.cos(2 * np.pi * i * forecast_linspace) for i in self.get_frequencies(p1)], dtype=np.float32),
) # H/2-1
s2_f = torch.tensor(
[np.sin(2 * np.pi * i * forecast_linspace) for i in self.get_frequencies(p2)], dtype=torch.float32
s2_f = torch.from_numpy(np.array(
[np.sin(2 * np.pi * i * forecast_linspace) for i in self.get_frequencies(p2)], dtype=np.float32),
)
self.register_buffer("S_forecast", torch.cat([s1_f, s2_f]))

Expand Down Expand Up @@ -151,10 +151,13 @@ def __init__(
backcast_linspace, forecast_linspace = linspace(backcast_length, forecast_length, centered=True)
norm = np.sqrt(forecast_length / thetas_dim) # ensure range of predictions is comparable to input

coefficients = torch.tensor([backcast_linspace**i for i in range(thetas_dim)], dtype=torch.float32)
coefficients = torch.from_numpy(
np.array([backcast_linspace**i for i in range(thetas_dim)], dtype=np.float32),
)
self.register_buffer("T_backcast", coefficients * norm)

coefficients = torch.tensor([forecast_linspace**i for i in range(thetas_dim)], dtype=torch.float32)
coefficients = torch.from_numpy(
np.array([forecast_linspace**i for i in range(thetas_dim)], dtype=np.float32),
)
self.register_buffer("T_forecast", coefficients * norm)

def forward(self, x) -> Tuple[torch.Tensor, torch.Tensor]:
Expand Down
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