R/`sl3`: modern Super Learning with pipelines

A modern implementation of the Super Learner algorithm for ensemble learning and model stacking

Authors: Jeremy Coyle, Nima Hejazi, Ivana Malenica, Oleg Sofrygin

What’s `sl3`?

sl3 is a modern implementation of the Super Learner algorithm of van der Laan, Polley, and Hubbard (2007). The Super Learner algorithm performs ensemble learning in one of two fashions:

The discrete Super Learner can be used to select the best prediction algorithm from among a supplied library of machine learning algorithms (“learners” in the sl3 nomenclature) – that is, the discrete Super Learner is the single learning algorithm that minimizes the cross-validated risk with respect to an appropriate loss function.
The ensemble Super Learner can be used to assign weights to a set of specified learning algorithms (from a user-supplied library of such algorithms) so as to create a combination of these learners that minimizes the cross-validated risk with respect to an appropriate loss function. This notion of weighted combinations has also been referred to as stacked regression (Breiman 1996) and stacked generalization (Wolpert 1992).

Installation

Install the most recent version from the master branch on GitHub via remotes:

remotes::install_github("tlverse/sl3")

Past stable releases may be located via the releases page on GitHub and may be installed by including the appropriate major version tag. For example,

remotes::install_github("tlverse/sl3@v1.3.5")

To contribute, check out the devel branch and consider submitting a pull request.

Issues

If you encounter any bugs or have any specific feature requests, please file an issue.

Examples

sl3 makes the process of applying screening algorithms, learning algorithms, combining both types of algorithms into a stacked regression model, and cross-validating this whole process essentially trivial. The best way to understand this is to see the sl3 package in action:

set.seed(49753)
library(tidyverse)
library(data.table)
library(SuperLearner)
library(origami)
library(sl3)

# load example data set
data(cpp)
cpp <- cpp %>%
  dplyr::filter(!is.na(haz)) %>%
  mutate_all(~ replace(., is.na(.), 0))

# use covariates of intest and the outcome to build a task object
covars <- c("apgar1", "apgar5", "parity", "gagebrth", "mage", "meducyrs",
            "sexn")
task <- sl3_Task$new(cpp, covariates = covars, outcome = "haz")

# set up screeners and learners via built-in functions and pipelines
slscreener <- Lrnr_pkg_SuperLearner_screener$new("screen.glmnet")
glm_learner <- Lrnr_glm$new()
screen_and_glm <- Pipeline$new(slscreener, glm_learner)
SL.glmnet_learner <- Lrnr_pkg_SuperLearner$new(SL_wrapper = "SL.glmnet")

# stack learners into a model (including screeners and pipelines)
learner_stack <- Stack$new(SL.glmnet_learner, glm_learner, screen_and_glm)
stack_fit <- learner_stack$train(task)
preds <- stack_fit$predict()
head(preds)
#>    Lrnr_pkg_SuperLearner_SL.glmnet Lrnr_glm_TRUE
#> 1:                      0.35618966    0.36298498
#> 2:                      0.35618966    0.36298498
#> 3:                      0.24964615    0.25993072
#> 4:                      0.24964615    0.25993072
#> 5:                      0.24964615    0.25993072
#> 6:                      0.03776486    0.05680264
#>    Pipeline(Lrnr_pkg_SuperLearner_screener_screen.glmnet->Lrnr_glm_TRUE)
#> 1:                                                            0.36228209
#> 2:                                                            0.36228209
#> 3:                                                            0.25870995
#> 4:                                                            0.25870995
#> 5:                                                            0.25870995
#> 6:                                                            0.05600958

Contributions

Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.

Citation

After using the sl3 R package, please cite the following:

    @manual{coyle2019sl3,
      author = {Coyle, Jeremy R and Hejazi, Nima S and Malenica, Ivana and
        Sofrygin, Oleg},
      title = {{sl3}: Modern Pipelines for Machine Learning and {Super
        Learning}},
      year = {2019},
      howpublished = {\url{https://github.com/tlverse/sl3}},
      note = {{R} package version 1.3.5},
      url = {https://doi.org/10.5281/zenodo.3558317},
      doi = {10.5281/zenodo.3558317}
    }

License

The contents of this repository are distributed under the GPL-3 license. See file LICENSE for details.

References

Breiman, Leo. 1996. “Stacked Regressions.” Machine Learning 24 (1). Springer: 49–64.

van der Laan, Mark J., Eric C. Polley, and Alan E. Hubbard. 2007. “Super Learner.” Statistical Applications in Genetics and Molecular Biology 6 (1).

Wolpert, David H. 1992. “Stacked Generalization.” Neural Networks 5 (2). Elsevier: 241–59.

ck37/sl3

R/sl3: modern Super Learning with pipelines

What’s sl3?