-
Notifications
You must be signed in to change notification settings - Fork 41
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
Document pickling acquisition functions #760
Conversation
@@ -168,4 +168,23 @@ def plot_ask_tell_regret(ask_tell_result): | |||
|
|||
|
|||
# %% [markdown] | |||
# A word of warning. This serialization technique is not guaranteed to work smoothly with every Tensorflow-based model, so apply to your own problems with caution. | |||
# In some more complicated scenarios we may also wish to serialise the acquisition function, rather than creating a new one from the models and data, as it may contain stochastic internal data. This is not an issue here (where we used the default `EfficientGlobalOptimization` rule and `ExpectedImprovement` function) but we can demonstrate it neverthless: |
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.
perhaps mention an acq function where there is an internal state?
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.
Can you remind me what the archetypal use case for this was?
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.
any that is using trajectory samplers I think, bcs they have internal state acq func has stochastic internal state... not sure about any others, perhaps one of the Henry's acq functions has internal states?
No description provided.