kangchenghou / gntk Goto Github PK
View Code? Open in Web Editor NEWImplementation of Graph Neural Tangent Kernel (NeurIPS 2019)
License: MIT License
Implementation of Graph Neural Tangent Kernel (NeurIPS 2019)
License: MIT License
clf = GridSearchCV(svc, {'C' : C_list}, cv=zip(train_fold_idx, test_fold_idx), n_jobs=80, verbose=0, return_train_score=True)
I think you need something similar for return_train_score=True on line:
clf = GridSearchCV(svc, {'C' : C_list}, cv=zip(train_fold_idx, test_fold_idx), n_jobs=80, verbose=0)
?
` if node_features != []:
node_features = np.stack(node_features)
node_feature_flag = True
else:
node_features = None
node_feature_flag = False
assert len(g) == n
g_list.append(S2VGraph(g, l, node_tags))`
So it looks like node_features is never really used, and node_tags is used. However, none of the other files gram.py, gntk.py, search.py etc use node_tags. And gntk uses node_features, which matches the paper, but its never set here.
Could you please let me know, how this method can be used on my own data-set. How can I format my data-set to fit into your model ?
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