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Update inference.py #1394

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3f07706
Update inference.py
tdktrang May 18, 2024
879d294
Update inference.py
tdktrang May 21, 2024
5590978
Update criterions.py
tdktrang May 21, 2024
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4 changes: 4 additions & 0 deletions PyTorch/Forecasting/TFT/criterions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,10 @@ def forward(self, predictions, targets):
return losses

def qrisk(pred, tgt, quantiles):
if isinstance(pred, torch.Tensor):
pred = pred.detach().cpu().numpy()
if isinstance(tgt, torch.Tensor):
tgt = tgt.detach().cpu().numpy()
diff = pred - tgt
ql = (1-quantiles)*np.clip(diff,0, float('inf')) + quantiles*np.clip(-diff,0, float('inf'))
losses = ql.reshape(-1, ql.shape[-1])
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5 changes: 4 additions & 1 deletion PyTorch/Forecasting/TFT/inference.py
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Expand Up @@ -111,7 +111,8 @@ def predict(args, config, model, data_loader, scalers, cat_encodings, extend_tar

def visualize_v2(args, config, model, data_loader, scalers, cat_encodings):
unscaled_predictions, unscaled_targets, ids, _ = predict(args, config, model, data_loader, scalers, cat_encodings, extend_targets=True)

unscaled_predictions = torch.tensor(unscaled_predictions)
unscaled_targets = torch.tensor(unscaled_targets)
num_horizons = config.example_length - config.encoder_length + 1
pad = unscaled_predictions.new_full((unscaled_targets.shape[0], unscaled_targets.shape[1] - unscaled_predictions.shape[1], unscaled_predictions.shape[2]), fill_value=float('nan'))
pad[:,-1,:] = unscaled_targets[:,-num_horizons,:]
Expand All @@ -138,6 +139,8 @@ def inference(args, config, model, data_loader, scalers, cat_encodings):
if args.joint_visualization or args.save_predictions:
ids = torch.from_numpy(ids.squeeze())
#ids = torch.cat([x['id'][0] for x in data_loader.dataset])
unscaled_predictions = torch.tensor(unscaled_predictions)
unscaled_targets = torch.tensor(unscaled_targets)
joint_graphs = torch.cat([unscaled_targets, unscaled_predictions], dim=2)
graphs = {i:joint_graphs[ids == i, :, :] for i in set(ids.tolist())}
for key, g in graphs.items(): #timeseries id, joint targets and predictions
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