The data scale in trainer isn't consistent
Factos001 opened this issue · 3 comments
Hi,
I found that in BasicTrainer.py, the scale of the model's outputs and labels isn't consistent when setting args.real_value=True. You may need to inverse transform the model's outputs to make it right.
Hi,
I found that in BasicTrainer.py, the scale of the model's outputs and labels isn't consistent when setting args.real_value=True. You may need to inverse transform the model's outputs to make it right.
Yeap, I also found this, you will find AGCRN model's label's scaler is really high while the output's is smaller. He forgets to inverse transform.
I still wonder why so many papers in Spatio-Temp's mae and rmse are all the same scaler !!! They use the real data to compare with the normalize data and got this answer, wtf??
Hi,
I found that in BasicTrainer.py, the scale of the model's outputs and labels isn't consistent when setting args.real_value=True. You may need to inverse transform the model's outputs to make it right.
Thanks for the comment. When setting args.real_value=True, the output is the real value, not the normalized value. The corresponding codes are Line 47, 74, and 182 in BasicTrainer.py.