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:

  1. 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.
  2. 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 stable release from GitHub via devtools:


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)
suppressMessages(library(data.table))
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:data.table':
#> 
#>     between, first, last
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(SuperLearner)
#> Loading required package: nnls
#> Super Learner
#> Version: 2.0-24
#> Package created on 2018-08-10
library(origami)
#> origami: Generalized Cross-Validation Framework
#> Version: 1.0.0
library(sl3)

# load example data set
data(cpp)
cpp <- cpp %>%
  dplyr::filter(!is.na(haz)) %>%
  mutate_all(funs(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)
#> Loading required package: glmnet
#> Loading required package: Matrix
#> Loading required package: foreach
#> Loaded glmnet 2.0-16
preds <- stack_fit$predict()
head(preds)
#>    Lrnr_pkg_SuperLearner_SL.glmnet Lrnr_glm_TRUE
#> 1:                      0.35345519    0.36298498
#> 2:                      0.35345519    0.36298498
#> 3:                      0.24554305    0.25993072
#> 4:                      0.24554305    0.25993072
#> 5:                      0.24554305    0.25993072
#> 6:                      0.02953193    0.05680264
#>    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

It is our hope that sl3 will grow to be widely used for creating stacked regression models and the cross-validation of pipelines that make up such models, as well as the variety of other applications in which the Super Learner algorithm plays a role. To that end, contributions are very welcome, though we ask that interested contributors consult our contribution guidelines prior to submitting a pull request.


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

    @misc{coyle2018sl3,
      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  = {2018},
      howpublished = {\url{https://github.com/tlverse/sl3}},
      note = {{R} package version 1.1.0},
      url = {https://doi.org/10.5281/zenodo.1342294},
      doi = {10.5281/zenodo.1342294}
    }

License

© 2017-2018 Jeremy R. Coyle, Nima S. Hejazi, Ivana Malenica, Oleg Sofrygin

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.