gmjmcmc's People
gmjmcmc's Issues
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.
Implement dynamic proposals
Proposals should be updated with a rolling MCMC estimate based on the current run.
Allow running the algorithms without adding an intercept.
See title.
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 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
Defaults for algorithm
Set defaults to make main algorithm function easier to begin with
Implement local optimizers
Simulated Annealing, Greedy algorithm and MT MCMC must be implemented to be able to continue testing.
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.
problems with predictions when the desired max.pop not reached
add a warning when pop size is below max.pop at the end
plot and summary after merge results
see the tile
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
Implement alpha generation
Implementation of three techniques from the paper by Hubin et. al. and the fully Bayesian version.
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
- ...
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).
add formulas to the GMJMCMC and MJMCMC as an alternative
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.
allow gen.params to $loglik$r
see the tile
Adding default likelihoods as binomial or gaussian
parallelisation on windows
a bit far fetched, yet nice to have in the future
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.
make predictions for a single threaded call of GMJMCMC
How should "private" functions be documented?
Should private functions have roxygen2 documentation, or can it perhaps be excluded?
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