Comments (4)
At this exact moment I have no time to take a detailed look.
However, I from a quick look I am led to believe that the problem is that we're not using the value_scale
parameter in the loss functions:
adaptive/adaptive/learner/learnerND.py
Lines 110 to 169 in d2c8041
This should be a relatively easy fix.
@bonh, unrelated to this issue, how is your experience with sampling a 5D space? Does Adaptive produce good results, better results than random sampling or uniform sampling? Personally, I have not even tried running real simulations beyond 3D, always thinking that "the curse of dimensionally" would bite me.
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@bonh thanks for your post. Could you perhaps share some more details? Are you talking about the Learner2D
? If so, the values should already be rescaled automatically.
It would be great if you could share some code!
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I use LearnerND
with 5 inputs ranging from
The function is quite complex and I'm not able to share it (yet). I'll try to find a MWE.
What I noticed was, that in the nonscaled problem, the values chosen by adaptive
did only vary by three or four digits right from the decimal point from one iteration to the next (until it failed).
from adaptive.
we're not using the value_scale parameter in the loss functions
That'd explain my observations, thanks!
I Just started sampling a 5D space, before that it was 3D, too. My target is to train a surrogate approximating my complex, costly function. However, the function is not that costly that I cannot sample 4000 points in a reasonable time. My guess is, that I would get similar results with different sampling procedures because I'm filling the parameter space very well. So I didn't do a detailed analysis but I think that I require about 30 % less samples with adaptive
compared to uniform (this is for 3D) to get comparable predictive accuracy with the trained surrogates.
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Related Issues (20)
- Allow to choose colormap in learner2D.plot() HOT 2
- Question: plot_trisurf (matplotlib) directly from qhull HOT 3
- Incompatibility of adaptive (asyncio) with python=3.10 HOT 4
- Stop using atomic writes HOT 2
- Documentation: use cases of coroutine by Learner and Runner not properly explained HOT 2
- Rename master branch to main HOT 3
- Fix branch name (master --> main) in binder link in readme HOT 1
- No module named 'typing_extensions'" HOT 2
- Learner2D.interpolator and Learner2D.interpolated_on_grid give different results HOT 5
- Target function returns NaN HOT 5
- Use in script with BlockingRunner: get log and/or feedback on progress HOT 4
- Handling with regions unreachable inside the `ConvexHull` in `LearnerND` HOT 2
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- Create API for just signle process (No pickle) HOT 2
- Efficient sampling of measurment bound functions: BatchExecutor? HOT 2
- Question on uncertainty quantification HOT 2
- Issues with Multiprocess and AsyncRunner in adaptive for Phase Diagram Illustration HOT 2
- Async Running Problem with AsyncRunner HOT 2
- Question: is this applicable for time series?
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