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

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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 among a supplied library of learning algorithms (“learners” in the sl3 nomenclature) – that is, that algorithm which minimizes the cross-validated risk with respect to some appropriate loss function.
2. The “ensemble” Super Learner can be used to assign weights to specified learning algorithms (in a user-supplied library) in order 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 called stacked regression (Breiman 1996).

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Installation

Install the most recent stable release from GitHub via devtools:

devtools::install_github("tlverse/sl3")

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Issues

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

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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

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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.

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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}
    }

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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.

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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).