is '#Predict stop' in jtnn_dec.py topological prediction?

[anny0316]

Hello WenGong：
is '#Predict stop' in jtnn_dec.py topological prediction of this article?
Meanwhile, In mpn.py:
ATOM_FDIM = len(ELEM_LIST) + 6 + 5 + 4 + 1
BOND_FDIM = 5 + 6
MAX_NB = 6 #maximum degree of junction tree
but , in jtmpn.py:
ATOM_FDIM = len(ELEM_LIST) + 6 + 5 + 1
BOND_FDIM = 5
MAX_NB = 15 #maximum degree of the molecular graph
Why is there such a difference? Please give me your advices. Thank you very much.

[anny0316]

@wengong-jin, hello wengong, Don't you need to predict tree roots in the latest code? By default, in the code, the first node of each tree is the root, is it right?

[anny0316]

@wengong-jin, hello wengong, how do the following values come into being? by the way, how to realize 'Constrained Optimization' which is mentioned in the paper. thank you very much.

valid = 0.9991
unique@1000 = 1.0
unique@10000 = 0.9997
FCD/Test = 0.9770302413177916
SNN/Test = 0.522326049871644
Frag/Test = 0.9950979926332992
Scaf/Test = 0.8655089872053796
FCD/TestSF = 1.5980327517965094
SNN/TestSF = 0.4996388119246172
Frag/TestSF = 0.9926974330760409
Scaf/TestSF = 0.1174452677242035
IntDiv = 0.8562054073435843
IntDiv2 = 0.8503170074513857
Filters = 0.9743769392453208
logP = 0.02464815889709815
SA = 0.15781023266502325
QED = 2.1869624593648385e-05
NP = 0.0962078166269753
weight = 8.657725423864576