gmjmcmc's People
gmjmcmc's Issues
Adding default likelihoods as binomial or gaussian
How should "private" functions be documented?
Should private functions have roxygen2 documentation, or can it perhaps be excluded?
add formulas to the GMJMCMC and MJMCMC as an alternative
open blas threaring feature, not a bug
When the "normal" gmjmcmc function is running, calculating this one run will start a single process that accesses 11 threads (unfortunately I don't know why there are exactly 11) (see screenshot #1 for the corresponding display)
However, if the gmjmcmc.parallel function is called with a certain number of runs and cores, min(#runs, #cores) processes are started, but only access ONE thread at a time. (see screenshot #2 for the display after calling gmjmcmc.parallel with cores=8 and runs=20).
What I conclude from this: When calling the "normal" gmjmcmc function, my Macbook automatically accesses multiple threads to calculate a single run. However, when the gmjmcmc.parallel function is called, only a single thread is assigned to each run.
It is therefore not surprising that on my Macbook, for example, the gmjmcmc.parallel with Runs = Cores takes significantly longer than the "normal" gmjmcmc function with otherwise the same arguments.
I don't think the internal functionality of gmjmcmc.parallel is intended to work that way, but unfortunately I can't say more about it due to my lack of knowledge about the connection between R and the CPU. It's also quite possible that this behavior is due to some specific setting on my Macbook, but I thought you might still be interested in this because (at least on my Macbook) it shows the advantages of using gmjmcmc.parallel over the "normal " gmjmcmc function significantly reduced.
implement logliks with g - priors (copy from the old package)
progress bar occasionally stuck when running GMJMCMC
allow gen.params to $loglik$r
see the tile
Implement local optimizers
Simulated Annealing, Greedy algorithm and MT MCMC must be implemented to be able to continue testing.
transforms in summary
global transforms for printing the feature
summary(result)
Importance | Feature
##############################| sigmoid(1+1x5+1x5)
#########################| x2
##############################| x4
##############################| x5
##############################| x6
##############################| x7
##############################| x9
##############################| sigmoid(1+1x3+1x7+1x4+1x5)
##############################| x8
##############################| x1
Best population: 7 log marginal posterior: -7342.925
Report population: 10 log marginal posterior: -7342.925
feats.strings marg.probs
1 sigmoid(1+1x5+1x5) 1.0000000
2 x4 1.0000000
3 x5 1.0000000
4 x6 1.0000000
5 x7 1.0000000
6 x9 1.0000000
7 sigmoid(1+1x3+1x7+1x4+1x5) 1.0000000
8 x8 1.0000000
9 x1 1.0000000
10 x2 0.8519006
If you continue running the code, then the second time calling summary(result) yields
summary(result)
Importance | Feature
##############################| p0(1+1x5+1x5)
#########################| x2
##############################| x4
##############################| x5
##############################| x6
##############################| x7
##############################| x9
##############################| p0(1+1x3+1x7+1x4+1x5)
##############################| x8
##############################| x1
Best population: 7 log marginal posterior: -7342.925
Report population: 10 log marginal posterior: -7342.925
feats.strings marg.probs
1 p0(1+1x5+1x5) 1.0000000
2 x4 1.0000000
3 x5 1.0000000
4 x6 1.0000000
5 x7 1.0000000
6 x9 1.0000000
7 p0(1+1x3+1x7+1x4+1x5) 1.0000000
8 x8 1.0000000
9 x1 1.0000000
10 x2 0.8519006
Defaults for algorithm
Set defaults to make main algorithm function easier to begin with
Allow to remove features before starting the first population.
When starting gmjmcmc it should be possible to:
- Manually specify the set of covariates that should be included in the first population.
- Randomly select a set number of features for the first population.
This allows the algorithm to work with "wide" data where it is not feasible to include all covariates in the initial population.
Standardization of precalculated features at the beginning of each population?
Would it be useful to standardize the precalculated features of each pop? If so, (probably true) implement it.
refactor loglik.pi to target.pi or logik.marginal
refactor loglik.pi to target.pi or logik.marginal
Allow running the algorithms without adding an intercept.
See title.
Merge GMJMCMC and MJMCMC run function into one
GMJMCMC and MJMCMC do the same thing when running MJMCMC, this should be one function, which is called from either algorithm.
parallelisation on windows
a bit far fetched, yet nice to have in the future
Roll own likelihood computation or use BAS
At the moment we are using the standard GLM function in R for likelihood computation. It might be more efficient to use the one available from BAS (as Aliaksandr has done before), or completely implement a slimmed version of it that can have additional features to make it more efficient for our purposes.
Implement a predict function (for GMJMCMC)
Given a result from gmjmcmc, a function for prediction should be implemented, doing the following
- For the top N models in every thread make a prediction.
- Weight the predictions w.r.t. to the renormalised posteriors.
- Weight the aggregated posteriors across the threads (in the same way as when merging results).
probably we need to add "robust" loglik implementations with error handling
Implement alpha generation
Implementation of three techniques from the paper by Hubin et. al. and the fully Bayesian version.
populations of not max size at the end handle automatically and issue a warning
Implement a function for parallel runs
We should have a function that
- Run multiple threads of gmjmcmc from a single function call
- Add the possibility to save the parallel results to files
- Add a neat way to pass the results to the merge.results function
progress in parallel version
I am now running a parallel version on 40 cores. For some reason this takes much longer than the single core run although I am using more or less the same parameter settings. Is there any chance to get some idea about the progress of the algorithm even in case of using several cores? Perhaps some kind of information from each core when it goes into the feature generation mode of gmjmcmc? Something like
C1P1
C2P1
C3P1
C2P2
where C is the core and P relates to the population which is newly generated.
plot and summary after merge results
see the tile
make predictions for a single threaded call of GMJMCMC
c++ backend for s-irls-sgd
Add nice wrapper functions for standard models
Add wrapper functions which make it easy to run some standard models:
- Bayesian GLM
- BGNLM
- Logic regresssion
- Fractional polynomial
- Cox regresssion
- ...
problems with predictions when the desired max.pop not reached
add a warning when pop size is below max.pop at the end
Implement dynamic proposals
Proposals should be updated with a rolling MCMC estimate based on the current run.
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