-
Notifications
You must be signed in to change notification settings - Fork 0
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
added surrogate model #2
Conversation
Good point. You can create another function to correct the sign. |
return model | ||
|
||
return model, input_transform |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Please check whether this is correct, I am bit doubtful regarding the function returning
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
It's almost done, but I'll add some changes later on my next push.
@@ -79,6 +84,26 @@ def get_surrogate_model(self, X, y): | |||
returns: | |||
model: Surrogate model for the MOBOQM9 model. | |||
""" | |||
X_scaled = Standardize(X) |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Thanks for the changes. Instead of using Standardize, can you use input_transform
and output_transform
in the SingleTaskGP
model?
src/mobo_qm9.py
Outdated
else: | ||
raise ValueError("Unsupported kernel type. Supported types are 'RBF', 'Matern', and 'Tanimoto'.") | ||
|
||
model = SingleTaskGP(train_X, train_Y, likelihood=likelihood, kernel=kernel) |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
This is only for single target optimization, can you use ModelListGP
for multi-target?
Just have a doubt whether
should be kept in the get_surrogate_model function or not