jajcayn / pygpso Goto Github PK

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.

• create more general GPSurrogate class
• allow for other GPFlow models (e.g. SGVP for lot of data points)

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.

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...

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

Requires tensorflow >= 2.1 which should be stated in the requirements.txt

Especially after new features:

• show user-defined callbacks
• show and explain the number of evaluation repeats
• saving / loading / checkpoints / continuing
• maybe even stepping the algorithm for yourself, i.e. not use run method, but iteratively use tree selection, tree evaluation, and gp update steps

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

For this, the following is needed:

• saving of parameter space and its state (which leaves has been explored etc.)
• saving of GPSurrogate (both - list of points and GPR model) (WIP: #40)
• allow resuming with new conditions (i.e. number of evaluations etc.)

This 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.