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Merge pull request #154 from tlverse/devel
Update master from devel
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,42 +1,55 @@ | ||
| branches: | ||
| only: | ||
| - master | ||
| - devel | ||
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| env: | ||
| global: | ||
| - RGL_USE_NULL=TRUE | ||
| - PKG_CFLAGS="-O3 -Wall -pedantic" | ||
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| language: r | ||
| dist: trusty | ||
| sudo: required | ||
| cache: packages | ||
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| cran: "http://cran.rstudio.com" | ||
| warnings_are_errors: true | ||
| r_check_args: '' | ||
| r_build_args: '--no-manual' | ||
| r_check_args: '--no-build-vignettes --no-manual' | ||
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| r: | ||
| - release | ||
| - devel | ||
| before_install: Rscript -e 'update.packages(ask = FALSE)' | ||
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| before_install: | ||
| - sudo apt-get -y install python3-pip python-dev | ||
| - sudo pip install numpy | ||
| - sudo pip install tensorflow | ||
| - sudo pip install keras | ||
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| - sudo pip install numpy tensorflow keras | ||
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| r_packages: | ||
| - devtools | ||
| - covr | ||
| - drat | ||
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| r_github_packages: | ||
| - jimhester/covr | ||
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| script: | ||
| - Rscript -e "devtools::install_github(c('osofr/condensier', | ||
| 'tlverse/delayed', | ||
| 'tlverse/origami', | ||
| 'jeremyrcoyle/hal9001'), | ||
| upgrade_dependencies = FALSE)" | ||
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| #r_github_packages: | ||
| #- osofr/condensier | ||
| #- tlverse/delayed | ||
| #- tlverse/origami | ||
| #- jeremyrcoyle/hal9001 | ||
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| after_success: | ||
| - travis_wait Rscript -e 'covr::codecov()' | ||
| - test $TRAVIS_PULL_REQUEST == "true" && test $TRAVIS_BRANCH == "master" && bash deploy.sh | ||
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| on_failure: | ||
| - travis_wait 80 Rscript -e 'covr::codecov()' | ||
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| _failure: | ||
| - "./travis-tool.sh dump_logs" | ||
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| notifications: | ||
| email: | ||
| on_success: change | ||
| on_failure: change | ||
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| env: | ||
| global: | ||
| - RGL_USE_NULL=TRUE | ||
| - PKG_CFLAGS="-O3 -Wall -pedantic" | ||
| - secure: 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 | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,107 @@ | ||
| #' BART Machine Learner | ||
| #' | ||
| #' This learner implements Bayesian Additive Regression Trees, using the | ||
| #' \code{bartMachine} package. | ||
| #' | ||
| #' @docType class | ||
| #' | ||
| #' @importFrom R6 R6Class | ||
| #' @importFrom stats predict | ||
| #' @importFrom assertthat assert_that is.count is.flag | ||
| #' | ||
| #' @export | ||
| #' | ||
| #' @keywords data | ||
| #' | ||
| #' @return Learner object with methods for training and prediction. See | ||
| #' \code{\link{Lrnr_base}} for documentation on learners. | ||
| #' | ||
| #' @format \code{\link{R6Class}} object. | ||
| #' | ||
| #' @family Learners | ||
| #' | ||
| #' @section Parameters: | ||
| #' \describe{ | ||
| #' \item{\code{Y}}{Outcome variable.} | ||
| #' \item{\code{X}}{Covariate dataframe.} | ||
| #' \item{\code{newX}}{Optional dataframe to predict the outcome.} | ||
| #' \item{\code{obsWeights}}{Optional observation-level weights (supported but not tested).} | ||
| #' \item{\code{id}}{Optional id to group observations from the same unit (not used | ||
| #' currently).} | ||
| #' \item{\code{family}}{"gaussian" for regression, "binomial" for binary classification.} | ||
| #' \item{\code{num_trees }}{The number of trees to be grown in the sum-of-trees model.} | ||
| #' \item{\code{num_burn_in}}{Number of MCMC samples to be discarded as "burn-in".} | ||
| #' \item{\code{num_iterations_after_burn_in}}{Number of MCMC samples to draw from the | ||
| #' posterior distribution of f(x).} | ||
| #' \item{\code{alpha}}{Base hyperparameter in tree prior for whether a node is | ||
| #' nonterminal or not.} | ||
| #' \item{\code{beta}}{Power hyperparameter in tree prior for whether a node is | ||
| #' nonterminal or not.} | ||
| #' \item{\code{k}}{For regression, k determines the prior probability that E(Y|X) is | ||
| #' contained in the interval (y_{min}, y_{max}), based on a normal | ||
| #' distribution. For example, when k=2, the prior probability is 95\%. For | ||
| #' classification, k determines the prior probability that E(Y|X) is between | ||
| #' (-3,3). Note that a larger value of k results in more shrinkage and a more | ||
| #' conservative fit.} | ||
| #' \item{\code{q}}{Quantile of the prior on the error variance at which the data-based | ||
| #' estimate is placed. Note that the larger the value of q, the more | ||
| #' aggressive the fit as you are placing more prior weight on values lower | ||
| #' than the data-based estimate. Not used for classification.} | ||
| #' \item{\code{nu}}{Degrees of freedom for the inverse chi^2 prior. Not used for | ||
| #' classification.} | ||
| #' \item{\code{verbose }}{Prints information about progress of the algorithm to the | ||
| #' screen.} | ||
| #' | ||
| #' } | ||
| #' | ||
| #' @template common_parameters | ||
| # | ||
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| Lrnr_bartMachine <- R6Class( | ||
| classname = "Lrnr_bartMachine", | ||
| inherit = Lrnr_base, portable = TRUE, class = TRUE, | ||
| public = list( | ||
| initialize = function(num_trees = 50, num_burn_in = 250, verbose = F, | ||
| alpha = 0.95, beta = 2, k = 2, q = 0.9, nu = 3, | ||
| num_iterations_after_burn_in = 1000, | ||
| prob_rule_class = 0.5, ...) { | ||
| super$initialize(params = args_to_list(), ...) | ||
| } | ||
| ), | ||
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| private = list( | ||
| .properties = c("continuous", "binomial", "categorical", "weights"), | ||
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| .train = function(task) { | ||
| args <- self$params | ||
| outcome_type <- self$get_outcome_type(task) | ||
|
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| # specify data | ||
| args$X <- as.data.frame(task$X) | ||
| args$y <- outcome_type$format(task$Y) | ||
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| if (task$has_node("weights")) { | ||
| args$weights <- task$weights | ||
| } | ||
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| if (task$has_node("offset")) { | ||
| args$offset <- task$offset | ||
| } | ||
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| fit_object <- call_with_args(bartMachine::bartMachine, args) | ||
|
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| return(fit_object) | ||
| }, | ||
|
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| .predict = function(task) { | ||
| # outcome_type <- private$.training_outcome_type | ||
| predictions <- stats::predict( | ||
| private$.fit_object, | ||
| new_data = data.frame(task$X) | ||
| ) | ||
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| return(predictions) | ||
| }, | ||
| .required_packages = c("rJava", "bartMachine") | ||
| ) | ||
| ) |
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