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View Code? Open in Web Editor NEWGaussian-Processes Surrogate Optimisation in python
License: MIT License
Gaussian-Processes Surrogate Optimisation in python
License: MIT License
GPSurrogate
classGPFlow
models (e.g. SGVP for lot of data points)Optional user-defined functions such as _post_initialise()
, _pre_iteration()
, _post_iteration()
, _post_update()
, _pre_finalise()
where user can define hers/his own callbacks. They would default at pass
.
A typical use-case would be plotting ternary tree after each iteration (i.e. in _post_iteration()
), special logging as per user, saving after each iteration, etc...
I'm having installation problems of igraph with the following error, linked to this igraph issue.
clang: warning: libstdc++ is deprecated; move to libc++ with a minimum deployment target of OS X 10.9 [-Wdeprecated]
ld: library not found for -lstdc++
clang: error: linker command failed with exit code 1 (use -v to see invocation)
error: command 'gcc' failed with exit status 1
Solution:
brew install igraph
MACOSX_DEPLOYMENT_TARGET=10.14 pip install python-igraph
I'm having installation problems of igraph with the following error, linked to this igraph issue.
clang: warning: libstdc++ is deprecated; move to libc++ with a minimum deployment target of OS X 10.9 [-Wdeprecated]
ld: library not found for -lstdc++
clang: error: linker command failed with exit code 1 (use -v to see invocation)
error: command 'gcc' failed with exit status 1
Solution:
brew install igraph
MACOSX_DEPLOYMENT_TARGET=10.14 pip install python-igraph
Currently not really possible due to GPFlow, but checkpoints from tensorflow should work.
Requires tensorflow >= 2.1
which should be stated in the requirements.txt
Especially after new features:
run
method, but iteratively use tree selection, tree evaluation, and gp update stepsThis stems from the LBSM: when doing an optimisation of the biophysical model, it might be useful to save the full model output (i.e. timeseries) along with the parameter to an external file.
Probably will use hdf
and tables
for this.
In some modelling cases, the objective function can be strongly stochastic (due to the nature of the model). In that case, each evaluation might introduce a non-negligible error.
One obvious solution would be to add (parallelized of course) multiple evaluations of the same point and in the end, take their mean/median/whatever as the actual evaluated score.
Requires minor edition of the source code, since some parallelisation is already done when initialising.
In some occasions, the assertion within the LeafNode
class throws, that the centers of middle child and parents are not the same when splitting using a ternary partition.
See the traceback error send by one of the users:
Possible culprit: floating-point errors when the tree is really deep.
Possible solution: all-close should be enough
For this, the following is needed:
GPSurrogate
(both - list of points and GPR model) (WIP: #40)A declarative, efficient, and flexible JavaScript library for building user interfaces.
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