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wassname
2020-10-18 13:33:24 +08:00
parent 279ef54d86
commit 975c27d5c3
4 changed files with 898 additions and 183 deletions
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seq2seq_time/predict.py
+2 -2
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@@ -17,7 +17,7 @@ def predict(model, ds_test, batch_size, device='cpu', scaler=None):
It's hard to use pandas for data with virtual dimensions so we will use xarray. Xarray has an interface similar to pandas but also allows coordinates which are virtual dimensions.
"""
load_test = torch.utils.data.dataloader.DataLoader(ds_test, batch_size=batch_size)
freq = ds_test.t.freq
freq = ds_test.df.index.freq
xrs = []
for i, batch in enumerate(tqdm(load_test, desc='predict')):
model.eval()
@@ -35,7 +35,7 @@ def predict(model, ds_test, batch_size, device='cpu', scaler=None):
# Make an xarray.Dataset for the data
bs = y_future.shape[0]
t_source = ds_test.t[i:i+bs].values
t_source = ds_test.df.index[i:i+bs].values
t_ahead = pd.timedelta_range(0, periods=ds_test.window_future, freq=freq).values
t_behind = pd.timedelta_range(end=-pd.Timedelta(freq), periods=ds_test.window_past, freq=freq)
xr_out = xr.Dataset(