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| Package: sl3 | ||
| Title: Machine Learning Pipelines | ||
| Version: 0.2 | ||
| Date: 2017-08-01 | ||
| Title: Pipelines for Machine Learning and Super Learning | ||
| Version: 0.2.1.0 | ||
| Authors@R: c( | ||
| person("Jeremy", "Coyle", email = "[email protected]", | ||
| role = c("aut", "cre", "cph")), | ||
| person("Nima", "Hejazi", email = "[email protected]", role = "aut"), | ||
| person("Oleg", "Sofrygin", email="[email protected]", role = "aut"), | ||
| person("Ivana", "Malenica", email = "[email protected]", role = "aut") | ||
| person("Jeremy", "Coyle", email = "[email protected]", | ||
| role = c("aut", "cre", "cph")), | ||
| person("Nima", "Hejazi", email = "[email protected]", role = "aut"), | ||
| person("Oleg", "Sofrygin", email="[email protected]", role = "aut"), | ||
| person("Ivana", "Malenica", email = "[email protected]", role = "aut") | ||
| ) | ||
| Maintainer: Jeremy Coyle <[email protected]> | ||
| Description: Implements the super learner prediction method and contains a | ||
| library of prediction algorithms to be used in the super learner. | ||
| License: GPL-3 | ||
| URL: https://github.com/jeremyrcoyle/sl3 | ||
| BugReports: https://github.com/jeremyrcoyle/sl3/issues | ||
| Depends: | ||
| R (>= 2.14.0) | ||
| library of prediction algorithms to be used in the super learner. | ||
| Depends: R (>= 2.14.0) | ||
| Imports: | ||
| data.table, | ||
| assertthat, | ||
| origami, | ||
| future, | ||
| R6, | ||
| uuid, | ||
| memoise, | ||
| digest, | ||
| BBmisc, | ||
| delayed, | ||
| methods, | ||
| stats | ||
| data.table, | ||
| assertthat, | ||
| origami, | ||
| future, | ||
| R6, | ||
| uuid, | ||
| memoise, | ||
| digest, | ||
| BBmisc, | ||
| delayed, | ||
| methods, | ||
| stats | ||
| Suggests: | ||
| testthat, | ||
| knitr, | ||
| rmarkdown, | ||
| glmnet, | ||
| devtools, | ||
| dplyr, | ||
| rgl, | ||
| Rsolnp, | ||
| condensier, | ||
| cvAUC, | ||
| h2o, | ||
| xgboost, | ||
| forecast, | ||
| nloptr, | ||
| rugarch, | ||
| nnls, | ||
| SuperLearner, | ||
| tsDyn, | ||
| randomForest | ||
| testthat, | ||
| knitr, | ||
| rmarkdown, | ||
| glmnet, | ||
| devtools, | ||
| dplyr, | ||
| rgl, | ||
| Rsolnp, | ||
| condensier, | ||
| cvAUC, | ||
| h2o, | ||
| xgboost, | ||
| forecast, | ||
| nloptr, | ||
| rugarch, | ||
| nnls, | ||
| SuperLearner, | ||
| tsDyn, | ||
| randomForest | ||
| Remotes: | ||
| github::osofr/condensier, | ||
| github::jeremyrcoyle/delayed | ||
| github::osofr/condensier, | ||
| github::jeremyrcoyle/delayed | ||
| License: GPL-3 | ||
| URL: https://github.com/jeremyrcoyle/sl3 | ||
| BugReports: https://github.com/jeremyrcoyle/sl3/issues | ||
| Encoding: UTF-8 | ||
| LazyData: yes | ||
| LazyLoad: yes | ||
| VignetteBuilder: knitr | ||
| RoxygenNote: 6.0.1.9000 |
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| #' GLM Fits | ||
| #' | ||
| #' This learner provides fitting procedures for generalized linear models by way | ||
| #' of a wrapper relying on \code{stats::glm}. | ||
| #' | ||
| #' @docType class | ||
| #' | ||
| #' @keywords data | ||
| #' | ||
| #' @return \code{\link{Lrnr_base}} object with methods for training and | ||
| #' prediction. | ||
| #' | ||
| #' @format \code{\link{R6Class}} object. | ||
| #' | ||
| #' @field family A \code{family} object from package \code{stats} describing the | ||
| #' error family of the model to be fit. See the documentation for the package | ||
| #' \code{stats} for details, or consult \code{stats::family} directly. | ||
| #' @field ... Additional arguments. | ||
| #' | ||
| #' @importFrom R6 R6Class | ||
| #' @importFrom stats glm predict family | ||
| #' | ||
| #' @export | ||
| #' @rdname undocumented_learner | ||
| Lrnr_glm <- R6Class(classname = "Lrnr_glm", inherit = Lrnr_base, portable = TRUE, | ||
| class = TRUE, public = list( | ||
| initialize = function(family = gaussian(), ...) { | ||
| params <- list(family = family, ...) | ||
| super$initialize(params = params, ...) | ||
| }), | ||
| private = list( | ||
| .train = function(task) { | ||
| params <- self$params | ||
| family <- params[["family"]] | ||
| if (is.character(family)) { | ||
| family <- get(family, mode = "function", envir = parent.frame()) | ||
| family <- family() | ||
| } | ||
| # todo: if possible have this use task$Xmat with glm.fit or speedglm | ||
| Y <- task$Y | ||
| fit_object <- glm(Y ~ ., data = task$X, family = family, weights = task$weights) | ||
| return(fit_object) | ||
|
|
||
| }, | ||
| .predict = function(task = NULL) { | ||
| predictions <- predict(private$.fit_object, newdata = task$X, type = "response") | ||
| return(predictions) | ||
| } | ||
| ), | ||
| # | ||
| Lrnr_glm <- R6Class(classname = "Lrnr_glm", inherit = Lrnr_base, | ||
| portable = TRUE, class = TRUE, | ||
| public = list( | ||
| initialize = function(family = gaussian(), ...) { | ||
| params <- list(family = family, ...) | ||
| super$initialize(params = params, ...) | ||
| } | ||
| ), | ||
| private = list( | ||
| .train = function(task) { | ||
| params <- self$params | ||
| family <- params[["family"]] | ||
| if (is.character(family)) { | ||
| family <- get(family, mode = "function", envir = parent.frame()) | ||
| family <- stats::family() | ||
| } | ||
| # TODO: if possible have this use task$Xmat with glm.fit or speedglm | ||
| Y <- task$Y | ||
| fit_object <- stats::glm(Y ~ ., data = task$X, family = family, | ||
| weights = task$weights) | ||
| return(fit_object) | ||
| }, | ||
| .predict = function(task = NULL) { | ||
| predictions <- stats::predict(private$.fit_object, newdata = task$X, | ||
| type = "response") | ||
| return(predictions) | ||
| } | ||
| ), | ||
| ) | ||
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