From 7a759d9dea29831ad03d9cabbd7fa1dcac44663b Mon Sep 17 00:00:00 2001 From: Joel Therrien Date: Sun, 30 Jun 2019 15:07:29 -0700 Subject: [PATCH] Version 1.0.1; cleaned up documentation. Moving towards a CRAN release. --- .Rbuildignore | 1 + DESCRIPTION | 17 +- NAMESPACE | 2 - NOTICE | 44 ++ R/cr_predictions.R | 6 +- R/loadForest.R | 4 +- R/misc.R | 4 +- R/predict.R | 6 +- R/regressionComponents.R | 9 - R/saveForest.R | 6 +- R/train.R | 332 +++++---- R/zzz.R | 9 +- .../randomforest/Bootstrapper.class | Bin 1273 -> 0 bytes .../randomforest/CovariateRow.class | Bin 4467 -> 0 bytes .../ca/joeltherrien/randomforest/Main.class | Bin 11471 -> 0 bytes .../ca/joeltherrien/randomforest/Row.class | Bin 4233 -> 0 bytes .../Settings$SettingsBuilder.class | Bin 4633 -> 0 bytes .../joeltherrien/randomforest/Settings.class | Bin 20522 -> 0 bytes .../randomforest/VisibleForTesting.class | Bin 359 -> 0 bytes .../Covariate$SplitRuleUpdater.class | Bin 868 -> 0 bytes .../covariates/Covariate$SplitUpdate.class | Bin 615 -> 0 bytes .../covariates/Covariate$Value.class | Bin 576 -> 0 bytes .../randomforest/covariates/Covariate.class | Bin 1865 -> 0 bytes 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Competing Risks Forests +Version: 1.0.1 +Authors@R: c( + person("Joel", "Therrien", email = "joel_therrien@sfu.ca", role = c("aut", "cre", "cph")), + person("Jiguo", "Cao", email = "jiguo_cao@sfu.ca", role = c("aut", "dgs")) + ) Description: This package is used for training competing risk random forests on larger scale datasets. - It currently only supports training models, running predictions, plotting those predictions (they are curves), + It currently supports training models, running predictions, plotting those predictions (they are curves), and some simple error analysis using concordance measures. License: GPL-3 +Copyright: All provided source code is copyrighted and owned by Joel Therrien. + There are two dependencies (partially provided) used in the Java code; both + of which are licensed under the Apache 2.0 License. Please see the NOTICE + file for more information. Encoding: UTF-8 LazyData: true Imports: @@ -14,6 +21,6 @@ Imports: Suggests: parallel, testthat -Depends: R (>= 3.4.2) +Depends: R (>= 3.4.0) SystemRequirements: Java JDK 1.8 or higher RoxygenNote: 6.1.1 diff --git a/NAMESPACE b/NAMESPACE index f728caa..2a9c930 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -15,8 +15,6 @@ S3method(print,CompetingRiskFunctions.List) S3method(print,JRandomForest) S3method(print,ResponseCombiner) S3method(print,SplitFinder) -S3method(train,default) -S3method(train,formula) export(CR_FunctionCombiner) export(CR_Response) export(CR_ResponseCombiner) diff --git a/NOTICE b/NOTICE new file mode 100644 index 0000000..3d3f692 --- /dev/null +++ b/NOTICE @@ -0,0 +1,44 @@ +# largeRCRF +Copyright 2018-2019 Joel Therrien + +largeRCRF is licensed under the GPL-3 license. + +largeRCRF contains in object form some Java classes from the +Apache Commons CSV project and Jackson JSON processor; none of +which were modified. + +Their copyright notices are displayed below, and the license files +they provided may be found in the licenses/ subfolder. + + + +Apache Commons CSV +Copyright 2005-2017 The Apache Software Foundation + +This product includes software developed at +The Apache Software Foundation (http://www.apache.org/). + + + + + +# Jackson JSON processor + +Jackson is a high-performance, Free/Open Source JSON processing library. +It was originally written by Tatu Saloranta (tatu.saloranta@iki.fi), and has +been in development since 2007. +It is currently developed by a community of developers, as well as supported +commercially by FasterXML.com. + +## Licensing + +Jackson core and extension components may be licensed under different licenses. +To find the details that apply to this artifact see the accompanying LICENSE file. +For more information, including possible other licensing options, contact +FasterXML.com (http://fasterxml.com). + +## Credits + +A list of contributors may be found from CREDITS file, which is included +in some artifacts (usually source distributions); but is always available +from the source code management (SCM) system project uses. diff --git a/R/cr_predictions.R b/R/cr_predictions.R index 6d0f03f..673c89a 100644 --- a/R/cr_predictions.R +++ b/R/cr_predictions.R @@ -48,7 +48,7 @@ extractCIF <- function (x, event) { #' @export extractCIF.CompetingRiskFunctions <- function(prediction, event){ - fun <- stepfun(prediction$time.interest, c(0, prediction$cif[,event])) + fun <- stats::stepfun(prediction$time.interest, c(0, prediction$cif[,event])) class(fun) <- "function" attr(fun, "call") <- sys.call() @@ -70,7 +70,7 @@ extractCHF <- function (x, event) { #' @export extractCHF.CompetingRiskFunctions <- function(prediction, event){ - fun <- stepfun(prediction$time.interest, c(0, prediction$chf[,event])) + fun <- stats::stepfun(prediction$time.interest, c(0, prediction$chf[,event])) class(fun) <- "function" attr(fun, "call") <- sys.call() @@ -93,7 +93,7 @@ extractSurvivorCurve <- function (x) { #' @export extractSurvivorCurve.CompetingRiskFunctions <- function(prediction){ - fun <- stepfun(prediction$time.interest, c(1, prediction$survivorCurve)) + fun <- stats::stepfun(prediction$time.interest, c(1, prediction$survivorCurve)) class(fun) <- "function" attr(fun, "call") <- sys.call() diff --git a/R/loadForest.R b/R/loadForest.R index d4600a2..ded2cde 100644 --- a/R/loadForest.R +++ b/R/loadForest.R @@ -4,10 +4,10 @@ #' #' Loads a random forest that was saved using \code{\link{saveForest}}. #' -#' @param forest The directory created that saved the previous forest. +#' @param directory The directory created that saved the previous forest. #' @return A JForest object; see \code{\link{train}} for details. #' @export -#' @seealso \code{\link{train}}, \code{\link{saveForest}}, \code{\link{loadForestArg}} +#' @seealso \code{\link{train}}, \code{\link{saveForest}} #' @examples #' # Regression Example #' x1 <- rnorm(1000) diff --git a/R/misc.R b/R/misc.R index c818704..2073041 100644 --- a/R/misc.R +++ b/R/misc.R @@ -78,10 +78,10 @@ plot.JMatrixPlottable <- function(mat, add=FALSE, type="s", xlab="Time", ylab=NU } - plot(mat[,2] ~ mat[,1], col=col, type=type, xlab=xlab, ylab=ylab, ...) + graphics::plot(mat[,2] ~ mat[,1], col=col, type=type, xlab=xlab, ylab=ylab, ...) } else{ - points(mat[,2] ~ mat[,1], col=col, type=type, xlab=xlab, ylab=ylab, ...) + graphics::points(mat[,2] ~ mat[,1], col=col, type=type, xlab=xlab, ylab=ylab, ...) } } diff --git a/R/predict.R b/R/predict.R index aa21ee1..bcd84bd 100644 --- a/R/predict.R +++ b/R/predict.R @@ -28,7 +28,8 @@ #' y <- 1 + x1 + x2 + rnorm(1000) #' #' data <- data.frame(x1, x2, y) -#' forest <- train(y ~ x1 + x2, data, ntree=100, numberOfSplits = 5, mtry = 1, nodeSize = 5) +#' forest <- train(y ~ x1 + x2, data, ntree=100, numberOfSplits = 5, +#' mtry = 1, nodeSize = 5) #' #' # Fix x2 to be 0 #' newData <- data.frame(x1 = seq(from=-2, to=2, by=0.5), x2 = 0) @@ -48,7 +49,8 @@ #' #' data <- data.frame(x1, x2) #' -#' forest <- train(CR_Response(delta, u) ~ x1 + x2, data, ntree=100, numberOfSplits=5, mtry=1, nodeSize=10) +#' forest <- train(CR_Response(delta, u) ~ x1 + x2, data, ntree=100, +#' numberOfSplits=5, mtry=1, nodeSize=10) #' newData <- data.frame(x1 = c(-1, 0, 1), x2 = 0) #' ypred <- predict(forest, newData) predict.JRandomForest <- function(forest, newData=NULL, parallel=TRUE, out.of.bag=NULL){ diff --git a/R/regressionComponents.R b/R/regressionComponents.R index 094d9cf..681ab6f 100644 --- a/R/regressionComponents.R +++ b/R/regressionComponents.R @@ -46,15 +46,6 @@ WeightedVarianceSplitFinder <- function(){ #' responseCombiner <- MeanResponseCombiner() #' # You would then use it in train() #' -#' # However; I'll show an internal Java method to make it clear what it does -#' # Note that you should never have to do the following -#' x <- 1:3 -#' x <- largeRCRF:::convertRListToJava(Numeric(x)) -#' -#' # will output a Java object containing 2 -#' output <- rJava::.jcall(responseCombiner$javaObject, "Ljava/lang/Double;", "combine", x) -#' responseCombiner$convertToRFunction(output) -#' MeanResponseCombiner <- function(){ javaObject <- .jnew(.class_MeanResponseCombiner) javaObject <- .jcast(javaObject, .class_ResponseCombiner) diff --git a/R/saveForest.R b/R/saveForest.R index 4b2eb71..af16761 100644 --- a/R/saveForest.R +++ b/R/saveForest.R @@ -3,7 +3,7 @@ #' Save Random Forests #' #' Saves a random forest for later use, given that the base R -#' \code{\link{base::save}} function doesn't work for this package. +#' \code{\link[base]{save}} function doesn't work for this package. #' #' @param forest The forest to save. #' @param directory The directory that should be created to save the trees in. @@ -24,8 +24,8 @@ #' forest <- train(y ~ x1 + x2, data, #' ntree=100, numberOfSplits = 5, mtry = 1, nodeSize = 5) #' -#' saveForest(forest, "trees") -#' new_forest <- loadForest("trees") +#' saveForest(forest, "saved_forest") +#' new_forest <- loadForest("saved_forest") saveForest <- function(forest, directory, overwrite=FALSE){ check_and_create_directory(directory, overwrite) diff --git a/R/train.R b/R/train.R index f0d3186..2cfa379 100644 --- a/R/train.R +++ b/R/train.R @@ -14,148 +14,11 @@ getCores <- function(){ return(cores) } -#' Train Random Forests -#' -#' Trains the random forest. The type of response the random forest can be -#' trained on varies depending on the \code{splitFinder}, -#' \code{nodeResponseCombiner}, and the \code{forestResponseCombiner} -#' parameters. Make sure these are compatible with each other, and with the -#' response you plug in. \code{splitFinder} should work on the responses you are -#' providing; \code{nodeResponseCombiner} should combine these responses into -#' some intermediate product, and \code{forestResponseCombiner} combines these -#' intermediate products into the final output product. Note that -#' \code{nodeResponseCombiner} and \code{forestResponseCombiner} can be inferred -#' from the data (so feel free to not specify them), and \code{splitFinder} can -#' be inferred but you might want to change its default. -#' -#' @param responses An R list of the responses. See \code{\link{CR_Response}} -#' for an example function. -#' @param data A data.frame containing the columns of the predictors and -#' responses; not relevant if you're not using the formula version of -#' \code{train}. -#' @param covariateData A data.frame containing only the columns of the -#' covariates you wish to use in your training (not relevant if you're using -#' the formula version of \code{train}). -#' @param splitFinder A split finder that's used to score splits in the random -#' forest training algorithm. See \code{\link{Competing Risk Split Finders}} -#' or \code{\link{WeightedVarianceSplitFinder}}. If you don't specify one, -#' this function tries to pick one based on the response. For -#' \code{\link{CR_Response}} without censor times, it will pick a -#' \code{\link{LogRankSplitFinder}}; while if censor times were provided it -#' will pick \code{\link{GrayLogRankSplitFinder}}; for integer or numeric -#' responses it picks a \code{\link{WeightedVarianceSplitFinder}}. -#' @param nodeResponseCombiner A response combiner that's used to combine -#' responses for each terminal node in a tree (regression example; average the -#' observations in each tree into a single number). See -#' \code{\link{CompetingRiskResponseCombiner}} or -#' \code{\link{MeanResponseCombiner}}. If you don't specify one, this function -#' tries to pick one based on the response. For \code{\link{CR_Response}} it -#' picks a \code{\link{CompetingRiskResponseCombiner}}; for integer or numeric -#' responses it picks a \code{\link{MeanResponseCombiner}}. -#' @param forestResponseCombiner A response combiner that's used to combine -#' predictions across trees into one final result (regression example; average -#' the prediction of each tree into a single number). See -#' \code{\link{CompetingRiskFunctionCombiner}} or -#' \code{\link{MeanResponseCombiner}}. If you don't specify one, this function -#' tries to pick one based on the response. For \code{\link{CR_Response}} it -#' picks a \code{\link{CompetingRiskFunctionCombiner}}; for integer or numeric -#' responses it picks a \code{\link{MeanResponseCombiner}}. -#' @param ntree An integer that specifies how many trees should be trained. -#' @param numberOfSplits A tuning parameter specifying how many random splits -#' should be tried for a covariate; a value of 0 means all splits will be -#' tried (with an exception for factors, who might have too many splits to -#' feasibly compute). -#' @param mtry A tuning parameter specifying how many covariates will be -#' randomly chosen to be tried in the splitting process. This value must be at -#' least 1. -#' @param nodeSize The algorithm will not attempt to split a node that has -#' observations less than 2*\code{nodeSize}; this guarantees that any two -#' sibling terminal nodes together have an average size of at least -#' \code{nodeSize}; note that it doesn't guarantee that every node is at least -#' as large as \code{nodeSize}. -#' @param maxNodeDepth This parameter is analogous to \code{nodeSize} in that it -#' controls tree length; by default \code{maxNodeDepth} is an extremely high -#' number and tree depth is controlled by \code{nodeSize}. -#' @param splitPureNodes This parameter determines whether the algorithm will -#' split a pure node. If set to FALSE, then before every split it will check -#' that every response is the same, and if so, not split. If set to TRUE it -#' forgoes that check and splits it. Prediction accuracy won't change under -#' any sensible \code{nodeResponseCombiner}; as all terminal nodes from a -#' split pure node should give the same prediction, so this parameter only -#' affects performance. If your response is continuous you'll likely -#' experience faster train times by setting it to TRUE. Default value is TRUE. -#' @param savePath If set, this parameter will save each tree of the random -#' forest in this directory as the forest is trained. Use this parameter if -#' you need to save memory while training. See also \code{\link{loadForest}} -#' @param savePath.overwrite This parameter controls the behaviour for what -#' happens if \code{savePath} is pointing to an existing directory. If set to -#' \code{warn} (default) then \code{train} refuses to proceed. If set to -#' \code{delete} then all the contents in that folder are deleted for the new -#' forest to be trained. Note that all contents are deleted, even those files -#' not related to \code{largeRCRF}. Use only if you're sure it's safe. If set -#' to \code{merge}, then the files describing the forest (such as its -#' parameters) are overwritten but the saved trees are not. The algorithm -#' assumes (without checking) that the existing trees are from a previous run -#' and starts from where it left off. This option is useful if recovering from -#' a crash. -#' @param cores This parameter specifies how many trees will be simultaneously -#' trained. By default the package attempts to detect how many cores you have -#' by using the \code{parallel} package and using all of them. You may specify -#' a lower number if you wish. It is not recommended to specify a number -#' greater than the number of available cores as this will hurt performance -#' with no available benefit. -#' @param randomSeed This parameter specifies a random seed if reproducible, -#' deterministic forests are desired. -#' @param displayProgress A logical indicating whether the progress should be -#' displayed to console; default is \code{TRUE}. Useful to set to FALSE in -#' some automated situations. -#' @export -#' @return A \code{JRandomForest} object. You may call \code{predict} or -#' \code{print} on it. -#' @seealso \code{\link{predict.JRandomForest}} -#' @note If saving memory is a concern, you can replace \code{covariateData} or -#' \code{data} with an environment containing one element called \code{data} -#' as the actual dataset. After the data has been imported into Java, but -#' before the forest training begins, the dataset in the environment is -#' deleted, freeing up memory in R. -#' @examples -#' # Regression Example -#' x1 <- rnorm(1000) -#' x2 <- rnorm(1000) -#' y <- 1 + x1 + x2 + rnorm(1000) -#' -#' data <- data.frame(x1, x2, y) -#' forest <- train(y ~ x1 + x2, data, WeightedVarianceSplitFinder(), MeanResponseCombiner(), MeanResponseCombiner(), ntree=100, numberOfSplits = 5, mtry = 1, nodeSize = 5) -#' -#' # Fix x2 to be 0 -#' newData <- data.frame(x1 = seq(from=-2, to=2, by=0.5), x2 = 0) -#' ypred <- predict(forest, newData) -#' -#' plot(ypred ~ newData$x1, type="l") -#' -#' # Competing Risk Example -#' x1 <- abs(rnorm(1000)) -#' x2 <- abs(rnorm(1000)) -#' -#' T1 <- rexp(1000, rate=x1) -#' T2 <- rweibull(1000, shape=x1, scale=x2) -#' C <- rexp(1000) -#' u <- pmin(T1, T2, C) -#' delta <- ifelse(u==T1, 1, ifelse(u==T2, 2, 0)) -#' -#' data <- data.frame(x1, x2) -#' -#' forest <- train(CR_Response(delta, u) ~ x1 + x2, data, -#' LogRankSplitFinder(1:2), CR_kResponseCombiner(1:2), CR_FunctionCombiner(1:2), ntree=100, numberOfSplits=5, mtry=1, nodeSize=10) -#' newData <- data.frame(x1 = c(-1, 0, 1), x2 = 0) -#' ypred <- predict(forest, newData) -train <- function(x, ...) UseMethod("train") - - - -#' @rdname train -#' @export -train.default <- function(responses, covariateData, splitFinder = splitFinderDefault(responses), nodeResponseCombiner = nodeResponseCombinerDefault(responses), forestResponseCombiner = forestResponseCombinerDefault(responses), ntree, numberOfSplits, mtry, nodeSize, maxNodeDepth = 100000, splitPureNodes=TRUE, savePath=NULL, savePath.overwrite=c("warn", "delete", "merge"), cores = getCores(), randomSeed = NULL, displayProgress = TRUE){ +train.internal <- function(responses, covariateData, splitFinder, + nodeResponseCombiner, forestResponseCombiner, ntree, + numberOfSplits, mtry, nodeSize, maxNodeDepth, + splitPureNodes, savePath, savePath.overwrite, + cores, randomSeed, displayProgress){ # Some quick checks on parameters ntree <- as.integer(ntree) @@ -187,6 +50,19 @@ train.default <- function(responses, covariateData, splitFinder = splitFinderDef if(is.null(savePath.overwrite) | length(savePath.overwrite)==0 | !(savePath.overwrite[1] %in% c("warn", "delete", "merge"))){ stop("savePath.overwrite must be one of c(\"warn\", \"delete\", \"merge\")") } + + if(is.null(splitFinder)){ + splitFinder <- splitFinderDefault(responses) + } + + if(is.null(nodeResponseCombiner)){ + nodeResponseCombiner <- nodeResponseCombinerDefault(responses) + } + + if(is.null(forestResponseCombiner)){ + forestResponseCombiner <- forestResponseCombinerDefault(responses) + } + if(class(nodeResponseCombiner) != "ResponseCombiner"){ @@ -287,9 +163,8 @@ train.default <- function(responses, covariateData, splitFinder = splitFinderDef } - - - forestObject <- list(call=match.call(), params=params, javaObject=forest.java, covariateList=dataset$covariateList, dataset=dataset$dataset) + forestObject <- list(params=params, javaObject=forest.java, + covariateList=dataset$covariateList, dataset=dataset$dataset) class(forestObject) <- "JRandomForest" return(forestObject) @@ -297,14 +172,147 @@ train.default <- function(responses, covariateData, splitFinder = splitFinderDef } - - -#' @rdname train -#' @export +#' Train Random Forests +#' +#' Trains the random forest. The type of response the random forest can be +#' trained on varies depending on the \code{splitFinder}, +#' \code{nodeResponseCombiner}, and the \code{forestResponseCombiner} +#' parameters. Make sure these are compatible with each other, and with the +#' response you plug in. \code{splitFinder} should work on the responses you are +#' providing; \code{nodeResponseCombiner} should combine these responses into +#' some intermediate product, and \code{forestResponseCombiner} combines these +#' intermediate products into the final output product. Note that +#' \code{nodeResponseCombiner} and \code{forestResponseCombiner} can be inferred +#' from the data (so feel free to not specify them), and \code{splitFinder} can +#' be inferred but you might want to change its default. +#' #' @param formula You may specify the response and covariates as a formula #' instead; make sure the response in the formula is still properly #' constructed; see \code{responses} -train.formula <- function(formula, data, ...){ +#' @param data A data.frame containing the columns of the predictors and +#' responses. +#' @param splitFinder A split finder that's used to score splits in the random +#' forest training algorithm. See \code{\link{CompetingRiskSplitFinders}} +#' or \code{\link{WeightedVarianceSplitFinder}}. If you don't specify one, +#' this function tries to pick one based on the response. For +#' \code{\link{CR_Response}} without censor times, it will pick a +#' \code{\link{LogRankSplitFinder}}; while if censor times were provided it +#' will pick \code{\link{GrayLogRankSplitFinder}}; for integer or numeric +#' responses it picks a \code{\link{WeightedVarianceSplitFinder}}. +#' @param nodeResponseCombiner A response combiner that's used to combine +#' responses for each terminal node in a tree (regression example; average the +#' observations in each tree into a single number). See +#' \code{\link{CR_ResponseCombiner}} or +#' \code{\link{MeanResponseCombiner}}. If you don't specify one, this function +#' tries to pick one based on the response. For \code{\link{CR_Response}} it +#' picks a \code{\link{CR_ResponseCombiner}}; for integer or numeric +#' responses it picks a \code{\link{MeanResponseCombiner}}. +#' @param forestResponseCombiner A response combiner that's used to combine +#' predictions across trees into one final result (regression example; average +#' the prediction of each tree into a single number). See +#' \code{\link{CR_FunctionCombiner}} or +#' \code{\link{MeanResponseCombiner}}. If you don't specify one, this function +#' tries to pick one based on the response. For \code{\link{CR_Response}} it +#' picks a \code{\link{CR_FunctionCombiner}}; for integer or numeric +#' responses it picks a \code{\link{MeanResponseCombiner}}. +#' @param ntree An integer that specifies how many trees should be trained. +#' @param numberOfSplits A tuning parameter specifying how many random splits +#' should be tried for a covariate; a value of 0 means all splits will be +#' tried (with an exception for factors, who might have too many splits to +#' feasibly compute). +#' @param mtry A tuning parameter specifying how many covariates will be +#' randomly chosen to be tried in the splitting process. This value must be at +#' least 1. +#' @param nodeSize The algorithm will not attempt to split a node that has +#' observations less than 2*\code{nodeSize}; this guarantees that any two +#' sibling terminal nodes together have an average size of at least +#' \code{nodeSize}; note that it doesn't guarantee that every node is at least +#' as large as \code{nodeSize}. +#' @param maxNodeDepth This parameter is analogous to \code{nodeSize} in that it +#' controls tree length; by default \code{maxNodeDepth} is an extremely high +#' number and tree depth is controlled by \code{nodeSize}. +#' @param splitPureNodes This parameter determines whether the algorithm will +#' split a pure node. If set to FALSE, then before every split it will check +#' that every response is the same, and if so, not split. If set to TRUE it +#' forgoes that check and splits it. Prediction accuracy won't change under +#' any sensible \code{nodeResponseCombiner}; as all terminal nodes from a +#' split pure node should give the same prediction, so this parameter only +#' affects performance. If your response is continuous you'll likely +#' experience faster train times by setting it to TRUE. Default value is TRUE. +#' @param savePath If set, this parameter will save each tree of the random +#' forest in this directory as the forest is trained. Use this parameter if +#' you need to save memory while training. See also \code{\link{loadForest}} +#' @param savePath.overwrite This parameter controls the behaviour for what +#' happens if \code{savePath} is pointing to an existing directory. If set to +#' \code{warn} (default) then \code{train} refuses to proceed. If set to +#' \code{delete} then all the contents in that folder are deleted for the new +#' forest to be trained. Note that all contents are deleted, even those files +#' not related to \code{largeRCRF}. Use only if you're sure it's safe. If set +#' to \code{merge}, then the files describing the forest (such as its +#' parameters) are overwritten but the saved trees are not. The algorithm +#' assumes (without checking) that the existing trees are from a previous run +#' and starts from where it left off. This option is useful if recovering from +#' a crash. +#' @param cores This parameter specifies how many trees will be simultaneously +#' trained. By default the package attempts to detect how many cores you have +#' by using the \code{parallel} package and using all of them. You may specify +#' a lower number if you wish. It is not recommended to specify a number +#' greater than the number of available cores as this will hurt performance +#' with no available benefit. +#' @param randomSeed This parameter specifies a random seed if reproducible, +#' deterministic forests are desired. +#' @param displayProgress A logical indicating whether the progress should be +#' displayed to console; default is \code{TRUE}. Useful to set to FALSE in +#' some automated situations. +#' @export +#' @return A \code{JRandomForest} object. You may call \code{predict} or +#' \code{print} on it. +#' @seealso \code{\link{predict.JRandomForest}} +#' @note If saving memory is a concern, you can replace \code{covariateData} or +#' \code{data} with an environment containing one element called \code{data} +#' as the actual dataset. After the data has been imported into Java, but +#' before the forest training begins, the dataset in the environment is +#' deleted, freeing up memory in R. +#' @examples +#' # Regression Example +#' x1 <- rnorm(1000) +#' x2 <- rnorm(1000) +#' y <- 1 + x1 + x2 + rnorm(1000) +#' +#' data <- data.frame(x1, x2, y) +#' forest <- train(y ~ x1 + x2, data, WeightedVarianceSplitFinder(), +#' MeanResponseCombiner(), MeanResponseCombiner(), ntree=100, +#' numberOfSplits = 5, mtry = 1, nodeSize = 5) +#' +#' # Fix x2 to be 0 +#' newData <- data.frame(x1 = seq(from=-2, to=2, by=0.5), x2 = 0) +#' ypred <- predict(forest, newData) +#' +#' plot(ypred ~ newData$x1, type="l") +#' +#' # Competing Risk Example +#' x1 <- abs(rnorm(1000)) +#' x2 <- abs(rnorm(1000)) +#' +#' T1 <- rexp(1000, rate=x1) +#' T2 <- rweibull(1000, shape=x1, scale=x2) +#' C <- rexp(1000) +#' u <- pmin(T1, T2, C) +#' delta <- ifelse(u==T1, 1, ifelse(u==T2, 2, 0)) +#' +#' data <- data.frame(x1, x2) +#' +#' forest <- train(CR_Response(delta, u) ~ x1 + x2, data, +#' LogRankSplitFinder(1:2), CR_ResponseCombiner(1:2), +#' CR_FunctionCombiner(1:2), ntree=100, numberOfSplits=5, +#' mtry=1, nodeSize=10) +#' newData <- data.frame(x1 = c(-1, 0, 1), x2 = 0) +#' ypred <- predict(forest, newData) +train <- function(formula, data, splitFinder = NULL, nodeResponseCombiner = NULL, + forestResponseCombiner = NULL, ntree, numberOfSplits, mtry, + nodeSize, maxNodeDepth = 100000, splitPureNodes=TRUE, savePath=NULL, + savePath.overwrite=c("warn", "delete", "merge"), cores = getCores(), + randomSeed = NULL, displayProgress = TRUE){ # Having an R copy of the data loaded at the same time can be wasteful; we # also allow users to provide an environment of the data which gets removed @@ -343,10 +351,10 @@ train.formula <- function(formula, data, ...){ } # Includes responses which we may need to later cut out - mf <- model.frame(formula=formula, data=data, na.action=na.pass) + mf <- stats::model.frame(formula=formula, data=data, na.action=stats::na.pass) if(is.null(responses)){ - responses <- model.response(mf) + responses <- stats::model.response(mf) } # remove any response variables @@ -356,13 +364,25 @@ train.formula <- function(formula, data, ...){ if(!is.null(env)){ env$data <- mf rm(data) - forest <- train.default(responses, env, ...) + forest <- train.internal(responses, env, splitFinder = splitFinder, + nodeResponseCombiner = nodeResponseCombiner, + forestResponseCombiner = forestResponseCombiner, + ntree = ntree, numberOfSplits = numberOfSplits, + mtry = mtry, nodeSize = nodeSize, maxNodeDepth = maxNodeDepth, + splitPureNodes = splitPureNodes, savePath = savePath, + savePath.overwrite = savePath.overwrite, cores = cores, + randomSeed = randomSeed, displayProgress = displayProgress) } else{ - forest <- train.default(responses, mf, ...) + forest <- train.internal(responses, mf, splitFinder = splitFinder, + nodeResponseCombiner = nodeResponseCombiner, + forestResponseCombiner = forestResponseCombiner, + ntree = ntree, numberOfSplits = numberOfSplits, + mtry = mtry, nodeSize = nodeSize, maxNodeDepth = maxNodeDepth, + splitPureNodes = splitPureNodes, savePath = savePath, + savePath.overwrite = savePath.overwrite, cores = cores, + randomSeed = randomSeed, displayProgress = displayProgress) } - - forest$call <- match.call() forest$formula <- formula diff --git a/R/zzz.R b/R/zzz.R index 9edb824..dee42b0 100644 --- a/R/zzz.R +++ b/R/zzz.R @@ -1,6 +1,11 @@ .onLoad <- function(libname, pkgname) { - # rJava needs to be initialized with the path to the class files - .jpackage(pkgname, lib.loc=libname, morePaths = "inst/java/") + # rJava expects a folder called 'java' to be present in the root of the + # package directory; but this isn't true when devtools::test() is run (instead + # it's in the inst folder which won't be present if the package was actually + # loaded from the R library). Thus with morePaths we make sure it loads up the + # jar in this situation. + .jpackage(pkgname, lib.loc=libname, morePaths="inst/java/largeRCRF-1.0-SNAPSHOT.jar") + } #' @import rJava diff --git a/inst/java/ca/joeltherrien/randomforest/Bootstrapper.class b/inst/java/ca/joeltherrien/randomforest/Bootstrapper.class deleted file mode 100644 index 20ed68538c4ce86eb403e7ff41a2f8a2f65cec24..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 1273 zcma)5T~E_c7=F&$ty0PaaT9g&jjo$e6hUQU#*dhgOiX0K$lcji$HCDqU8mr+zr-7F zHBkwg_yhbA#^2!u`kr=k6TIM}=k)!2p7-N?Km76yzzy8#Lm$%p7>B7LqeDSf$5qTq z&73?e9XjSUT+?u!!EilM6ArIG;67&7sD#wboAA_DZ~5C(W6nTjBK$ z?g=Tkl~LhT?Og`8N}pg*{VmaC$QCQy+IH=_zh!%#upP?_0H3>_-Sn+{9aat7BbKqy zC?UF4wi!}dhe31fSCSt?@?REvjSiFflS6j8GqCRS%8N(55fr0gfx$dMlRDwCyl{kH zU>NQV3fgFHFpM14hH=uA&X<$JdwaTjL6Quf%sx(KvY)^{kfa19#K;o_FR6ww`d?alyO&)G Z(k3ZTF0#cbHpH8z{NdjWl`lJghp diff --git a/inst/java/ca/joeltherrien/randomforest/CovariateRow.class 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If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/man/MeanResponseCombiner.Rd b/man/MeanResponseCombiner.Rd index 19cb8e1..113f804 100644 --- a/man/MeanResponseCombiner.Rd +++ b/man/MeanResponseCombiner.Rd @@ -18,13 +18,4 @@ regression. responseCombiner <- MeanResponseCombiner() # You would then use it in train() -# However; I'll show an internal Java method to make it clear what it does -# Note that you should never have to do the following -x <- 1:3 -x <- largeRCRF:::convertRListToJava(Numeric(x)) - -# will output a Java object containing 2 -output <- rJava::.jcall(responseCombiner$javaObject, "Ljava/lang/Double;", "combine", x) -responseCombiner$convertToRFunction(output) - } diff --git a/man/loadForest.Rd b/man/loadForest.Rd index ceaa29c..e571378 100644 --- a/man/loadForest.Rd +++ b/man/loadForest.Rd @@ -7,7 +7,7 @@ loadForest(directory) } \arguments{ -\item{forest}{The directory created that saved the previous forest.} +\item{directory}{The directory created that saved the previous forest.} } \value{ A JForest object; see \code{\link{train}} for details. @@ -29,5 +29,5 @@ saveForest(forest, "trees") new_forest <- loadForest("trees") } \seealso{ -\code{\link{train}}, \code{\link{saveForest}}, \code{\link{loadForestArg}} +\code{\link{train}}, \code{\link{saveForest}} } diff --git a/man/predict.JRandomForest.Rd b/man/predict.JRandomForest.Rd index 83f3297..adf4272 100644 --- a/man/predict.JRandomForest.Rd +++ b/man/predict.JRandomForest.Rd @@ -40,7 +40,8 @@ x2 <- rnorm(1000) y <- 1 + x1 + x2 + rnorm(1000) data <- data.frame(x1, x2, y) -forest <- train(y ~ x1 + x2, data, ntree=100, numberOfSplits = 5, mtry = 1, nodeSize = 5) +forest <- train(y ~ x1 + x2, data, ntree=100, numberOfSplits = 5, + mtry = 1, nodeSize = 5) # Fix x2 to be 0 newData <- data.frame(x1 = seq(from=-2, to=2, by=0.5), x2 = 0) @@ -60,7 +61,8 @@ delta <- ifelse(u==T1, 1, ifelse(u==T2, 2, 0)) data <- data.frame(x1, x2) -forest <- train(CR_Response(delta, u) ~ x1 + x2, data, ntree=100, numberOfSplits=5, mtry=1, nodeSize=10) +forest <- train(CR_Response(delta, u) ~ x1 + x2, data, ntree=100, + numberOfSplits=5, mtry=1, nodeSize=10) newData <- data.frame(x1 = c(-1, 0, 1), x2 = 0) ypred <- predict(forest, newData) } diff --git a/man/saveForest.Rd b/man/saveForest.Rd index 0efd402..2c8913c 100644 --- a/man/saveForest.Rd +++ b/man/saveForest.Rd @@ -18,7 +18,7 @@ default.} } \description{ Saves a random forest for later use, given that the base R -\code{\link{base::save}} function doesn't work for this package. +\code{\link[base]{save}} function doesn't work for this package. } \examples{ # Regression Example @@ -30,8 +30,8 @@ data <- data.frame(x1, x2, y) forest <- train(y ~ x1 + x2, data, ntree=100, numberOfSplits = 5, mtry = 1, nodeSize = 5) -saveForest(forest, "trees") -new_forest <- loadForest("trees") +saveForest(forest, "saved_forest") +new_forest <- loadForest("saved_forest") } \seealso{ \code{\link{train}}, \code{\link{loadForest}} diff --git a/man/train.Rd b/man/train.Rd index 1b08007..faa2542 100644 --- a/man/train.Rd +++ b/man/train.Rd @@ -2,33 +2,24 @@ % Please edit documentation in R/train.R \name{train} \alias{train} -\alias{train.default} -\alias{train.formula} \title{Train Random Forests} \usage{ -train(x, ...) - -\method{train}{default}(responses, covariateData, - splitFinder = splitFinderDefault(responses), - nodeResponseCombiner = nodeResponseCombinerDefault(responses), - forestResponseCombiner = forestResponseCombinerDefault(responses), - ntree, numberOfSplits, mtry, nodeSize, maxNodeDepth = 1e+05, - splitPureNodes = TRUE, savePath = NULL, +train(formula, data, splitFinder = NULL, nodeResponseCombiner = NULL, + forestResponseCombiner = NULL, ntree, numberOfSplits, mtry, nodeSize, + maxNodeDepth = 1e+05, splitPureNodes = TRUE, savePath = NULL, savePath.overwrite = c("warn", "delete", "merge"), cores = getCores(), randomSeed = NULL, displayProgress = TRUE) - -\method{train}{formula}(formula, data, ...) } \arguments{ -\item{responses}{An R list of the responses. See \code{\link{CR_Response}} -for an example function.} +\item{formula}{You may specify the response and covariates as a formula +instead; make sure the response in the formula is still properly +constructed; see \code{responses}} -\item{covariateData}{A data.frame containing only the columns of the -covariates you wish to use in your training (not relevant if you're using -the formula version of \code{train}).} +\item{data}{A data.frame containing the columns of the predictors and +responses.} \item{splitFinder}{A split finder that's used to score splits in the random -forest training algorithm. See \code{\link{Competing Risk Split Finders}} +forest training algorithm. See \code{\link{CompetingRiskSplitFinders}} or \code{\link{WeightedVarianceSplitFinder}}. If you don't specify one, this function tries to pick one based on the response. For \code{\link{CR_Response}} without censor times, it will pick a @@ -39,19 +30,19 @@ responses it picks a \code{\link{WeightedVarianceSplitFinder}}.} \item{nodeResponseCombiner}{A response combiner that's used to combine responses for each terminal node in a tree (regression example; average the observations in each tree into a single number). See -\code{\link{CompetingRiskResponseCombiner}} or +\code{\link{CR_ResponseCombiner}} or \code{\link{MeanResponseCombiner}}. If you don't specify one, this function tries to pick one based on the response. For \code{\link{CR_Response}} it -picks a \code{\link{CompetingRiskResponseCombiner}}; for integer or numeric +picks a \code{\link{CR_ResponseCombiner}}; for integer or numeric responses it picks a \code{\link{MeanResponseCombiner}}.} \item{forestResponseCombiner}{A response combiner that's used to combine predictions across trees into one final result (regression example; average the prediction of each tree into a single number). See -\code{\link{CompetingRiskFunctionCombiner}} or +\code{\link{CR_FunctionCombiner}} or \code{\link{MeanResponseCombiner}}. If you don't specify one, this function tries to pick one based on the response. For \code{\link{CR_Response}} it -picks a \code{\link{CompetingRiskFunctionCombiner}}; for integer or numeric +picks a \code{\link{CR_FunctionCombiner}}; for integer or numeric responses it picks a \code{\link{MeanResponseCombiner}}.} \item{ntree}{An integer that specifies how many trees should be trained.} @@ -113,14 +104,6 @@ deterministic forests are desired.} \item{displayProgress}{A logical indicating whether the progress should be displayed to console; default is \code{TRUE}. Useful to set to FALSE in some automated situations.} - -\item{formula}{You may specify the response and covariates as a formula -instead; make sure the response in the formula is still properly -constructed; see \code{responses}} - -\item{data}{A data.frame containing the columns of the predictors and -responses; not relevant if you're not using the formula version of -\code{train}.} } \value{ A \code{JRandomForest} object. You may call \code{predict} or @@ -153,7 +136,9 @@ x2 <- rnorm(1000) y <- 1 + x1 + x2 + rnorm(1000) data <- data.frame(x1, x2, y) -forest <- train(y ~ x1 + x2, data, WeightedVarianceSplitFinder(), MeanResponseCombiner(), MeanResponseCombiner(), ntree=100, numberOfSplits = 5, mtry = 1, nodeSize = 5) +forest <- train(y ~ x1 + x2, data, WeightedVarianceSplitFinder(), + MeanResponseCombiner(), MeanResponseCombiner(), ntree=100, + numberOfSplits = 5, mtry = 1, nodeSize = 5) # Fix x2 to be 0 newData <- data.frame(x1 = seq(from=-2, to=2, by=0.5), x2 = 0) @@ -174,7 +159,9 @@ delta <- ifelse(u==T1, 1, ifelse(u==T2, 2, 0)) data <- data.frame(x1, x2) forest <- train(CR_Response(delta, u) ~ x1 + x2, data, -LogRankSplitFinder(1:2), CR_kResponseCombiner(1:2), CR_FunctionCombiner(1:2), ntree=100, numberOfSplits=5, mtry=1, nodeSize=10) + LogRankSplitFinder(1:2), CR_ResponseCombiner(1:2), + CR_FunctionCombiner(1:2), ntree=100, numberOfSplits=5, + mtry=1, nodeSize=10) newData <- data.frame(x1 = c(-1, 0, 1), x2 = 0) ypred <- predict(forest, newData) } diff --git a/tests/testthat/test_adding_new_trees.R b/tests/testthat/test_adding_new_trees.R index 08a1172..c3bcf92 100644 --- a/tests/testthat/test_adding_new_trees.R +++ b/tests/testthat/test_adding_new_trees.R @@ -22,7 +22,9 @@ test_that("Can add trees on existing forest", { test_that("Test adding trees on saved forest - using delete", { - expect_false(file.exists("trees")) # Folder shouldn't exist yet + if(file.exists("trees")){ # folder could exist from a previous failed test; delete it + unlink("trees", recursive=TRUE) + } trainingData <- data.frame(x=rnorm(100)) trainingData$T <- rexp(100) + abs(trainingData$x) @@ -45,7 +47,9 @@ test_that("Test adding trees on saved forest - using delete", { test_that("Test adding trees on saved forest - using merge", { - expect_false(file.exists("trees")) # Folder shouldn't exist yet + if(file.exists("trees")){ # folder could exist from a previous failed test; delete it + unlink("trees", recursive=TRUE) + } trainingData <- data.frame(x=rnorm(100)) trainingData$T <- rexp(100) + abs(trainingData$x) diff --git a/tests/testthat/test_saving_offline.R b/tests/testthat/test_saving_offline.R index 123d157..1823d34 100644 --- a/tests/testthat/test_saving_offline.R +++ b/tests/testthat/test_saving_offline.R @@ -2,7 +2,9 @@ context("Make sure we can save forests while training") test_that("Can save a random forest while training, and use it afterward", { - expect_false(file.exists("trees")) # Folder shouldn't exist yet + if(file.exists("trees")){ # folder could exist from a previous failed test; delete it + unlink("trees", recursive=TRUE) + } x1 <- rnorm(1000) x2 <- rnorm(1000)

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