#ValueError: to get argmax of an empty sequence about deep-symbolic-optimization HOT 4 CLOSED

I have the same problem when i set y as a constant array

from deep-symbolic-optimization.

I did a bid digging myself and it seems like the issue comes from train.py. Specifically, the code can pick nan value as a threshold for risk seeking policy gradient. It is on lines 365-370 (approx). If one replaces np.quantile() with np.nanquantile(), then the code runs just fine (See below)

        else: # Empirical quantile   
            # IK added 02.03.2023   np.nanquantile                          
            # quantile = np.quantile(r, 1 - epsilon, interpolation="higher")
            quantile = np.nanquantile(r, 1 - epsilon, interpolation="higher")

from deep-symbolic-optimization.

Oh, this seems to make the code run. but the R value is now nan.

from deep-symbolic-optimization.

Thanks @IgorK7! Yea, we have run into some issues with nan. Good to know about np.nanquantile, though it still seems like an issue to have a nan reward. We have also found nan reward values sneak in from time to time, which can cause weird behaviors, but we haven't yet been able to nail down why; and it's hard to reproduce as it depends on the dataset... Replacing nan rewards with some bad negative reward might patch this, but still not a great fix...

from deep-symbolic-optimization.