OnlineSuperLearner: SuperLearner with online functionality for time-series analysis
This is the current version of the Online SuperLearner for Time-Series data R package. Note that this version is in active development, and considered to be pre-alpha software. Be very careful interpretting any results from this package.
- Automatic optimal predictor ensembling via sequential cross-validatio.
- Can be extended with several algorithms.
- Has several pre-defined summary measures
Install the development version from GitHub:
# install.packages("devtools") devtools::install_github("frbl/OnlineSuperLearner")
Rebuilding the documentation and webpage:
# Generate documentation devtools::document() # Build vignettes devtools::build_vignettes() # Rebuild webpage pkgdown::build_site()
For an example on how to run the OnlineSuperLearner, view the Jupyter notebook, or the
R/OnlineSuperLearner.Simulation.R file. For a complete guide see the documentation.
You can also run the demos for the project. Run:
demo('cpp-demo', package = 'OnlineSuperLearner')
The algorithm syntax
- Each entry should have an
algorithmentry with the name of the algorithm (i.e., the class name), and could have
algorithm_paramswhich is a list of hyperparameters for the specific algorithm. Furthermore, it could have a
paramsentry with two entries:
nbinsspecifying the number of bins to use for the discretization step, and
onlinea boolean specifying whether the algorithm should be treated as an online one.
algos <- append(algos, list(list(algorithm = 'ML.SVM', algorithm_params = list(), params = list(nbins = nbins, online = FALSE))))
The intervention syntax
You can specify interventions as follows:
intervention <- list(variable = 'A', when = c(2), what = c(1))
variable is the variable to perform the intervention on,
when is when the intervention should take place (at
t= 2 in this example) and
what what the intervention should be (1 in this case, but this could e.g. also be 0).
- View the issues page
Polley EC, van der Laan MJ (2010) Super Learner in Prediction. U.C. Berkeley Division of Biostatistics Working Paper Series. Paper 226. http://biostats.bepress.com/ucbbiostat/paper266/
van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007) Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25. http://www.degruyter.com/view/j/sagmb.2007.6.issue-1/sagmb.2007.6.1.1309/sagmb.2007.6.1.1309.xml
van der Laan, M. J., & Rose, S. (2011). Targeted learning: causal inference for observational and experimental data. Springer Science & Business Media. http://www.targetedlearningbook.com
Benkeser, D., Ju, C., Lendle, S. D., & van der Laan, M. J. (2016). Online Cross-Validation-Based Ensemble Learning. U.C. Berkeley Division of Biostatistics Working Paper Series, Paper 355. http://biostats.bepress.com/ucbbiostat/paper355/