Add support for 'offline' forests

crash.

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: largeRCRF
 Type: Package
 Title: Large Random Competing Risks Forests
-Version: 1.0.3.1
+Version: 1.0.5
 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"))
@@ -15,7 +15,8 @@ Copyright: All provided source code is copyrighted and owned by Joel Therrien.
 Encoding: UTF-8
 LazyData: true
 Imports:
-    rJava (>= 0.9-9)
+    rJava (>= 0.9-9),
+    stats (>= 3.4.0)
 Suggests:
     parallel,
     testthat,

NAMESPACE

@@ -1,5 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method("[",CompetingRiskResponses)
+S3method("[",CompetingRiskResponsesWithCensorTimes)
 S3method(extractCHF,CompetingRiskFunctions)
 S3method(extractCHF,CompetingRiskFunctions.List)
 S3method(extractCIF,CompetingRiskFunctions)
@@ -24,12 +26,15 @@ export(Numeric)
 export(WeightedVarianceSplitFinder)
 export(addTrees)
 export(connectToData)
+export(convertToOnlineForest)
 export(extractCHF)
 export(extractCIF)
 export(extractMortalities)
 export(extractSurvivorCurve)
+export(integratedBrierScore)
 export(loadForest)
 export(naiveConcordance)
 export(saveForest)
 export(train)
+export(vimp)
 import(rJava)

R/CR_Response.R

@@ -38,6 +38,51 @@ CR_Response <- function(delta, u, C = NULL){
 }
 
 
+# This function is useful is we ever want to do something like CR_Response(c(1,1,2), c(0.1,0.2,0.3))[1]
+#' @export
+"[.CompetingRiskResponses" <- function(object, indices){
+  newList <- list(
+    eventIndicator = object$eventIndicator[indices],
+    eventTime = object$eventTime[indices]
+    )
+  
+  previous.java.list <- object$javaObject
+  
+  new.java.list <- .jcall(.class_RUtils,
+                          makeResponse(.class_List),
+                          "produceSublist",
+                          previous.java.list,
+                          .jarray(as.integer(indices - 1)))
+  
+  newList$javaObject <- new.java.list
+  
+  class(newList) <- "CompetingRiskResponses"
+  return(newList)
+}
+
+# This function is useful is we ever want to do something like CR_Response(c(1,1,2), c(0.1,0.2,0.3), c(2,3,4))[1]
+#' @export
+"[.CompetingRiskResponsesWithCensorTimes" <- function(object, indices){
+  newList <- list(
+    eventIndicator = object$eventIndicator[indices],
+    eventTime = object$eventTime[indices],
+    censorTime = object$censorTime[indices]
+    )
+  
+  previous.java.list <- object$javaObject
+  
+  new.java.list <- .jcall(.class_RUtils,
+                          makeResponse(.class_List),
+                          "produceSublist",
+                          previous.java.list,
+                          .jarray(as.integer(indices - 1)))
+  
+  newList$javaObject <- new.java.list
+  
+  class(newList) <- "CompetingRiskResponsesWithCensorTimes"
+  return(newList)
+}
+
 # Internal function
 Java_CompetingRiskResponses <- function(delta, u){
   

R/addTrees.R

@@ -14,6 +14,10 @@
 #' @param savePath.overwrite If \code{savePath} is pointing to an existing
 #'   directory, possibly containing another forest, this specifies what should
 #'   be done.
+#' @param forest.output This parameter only applies if \code{savePath} has been
+#'   set; set to 'online' (default) and the saved forest will be loaded into
+#'   memory after being trained. Set to 'offline' and the forest is not saved
+#'   into memory, but can still be used in a memory unintensive manner.
 #' @param cores The number of cores to be used for training the new trees.
 #' @param displayProgress A logical indicating whether the progress should be
 #'   displayed to console; default is \code{TRUE}. Useful to set to FALSE in
@@ -22,7 +26,11 @@
 #' @return A new forest with the original and additional trees.
 #' @export
 #' 
-addTrees <- function(forest, numTreesToAdd, savePath = NULL, savePath.overwrite = c("warn", "delete", "merge"), cores = getCores(), displayProgress = TRUE){
+addTrees <- function(forest, numTreesToAdd, savePath = NULL,
+                     savePath.overwrite = c("warn", "delete", "merge"),
+                     forest.output = c("online", "offline"), 
+                     cores = getCores(), displayProgress = TRUE){
+  
   if(is.null(forest$dataset)){
     stop("Training dataset must be connected to forest before more trees can be added; this can be done manually by using connectToData")
   }
@@ -37,6 +45,10 @@ addTrees <- function(forest, numTreesToAdd, savePath = NULL, savePath.overwrite
     stop("savePath.overwrite must be one of c(\"warn\", \"delete\", \"merge\")")
   }
   
+  if(is.null(forest.output) | length(forest.output)==0 | !(forest.output[1] %in% c("online", "offline"))){
+    stop("forest.output must be one of c(\"online\", \"offline\")")
+  }
+  
   newTreeCount <- forest$params$ntree + as.integer(numTreesToAdd)
   
   treeTrainer <- createTreeTrainer(responseCombiner=forest$params$nodeResponseCombiner,
@@ -98,22 +110,23 @@ addTrees <- function(forest, numTreesToAdd, savePath = NULL, savePath.overwrite
                          params=params,
                          forestCall=match.call())
     
+    forest.java <- NULL
     if(cores > 1){
-      .jcall(forestTrainer, "V", "trainParallelOnDisk", initial.forest.optional, as.integer(cores))
+      forest.java <- .jcall(forestTrainer, makeResponse(.class_OfflineForest), "trainParallelOnDisk", initial.forest.optional, as.integer(cores))
     } else {
-      .jcall(forestTrainer, "V", "trainSerialOnDisk", initial.forest.optional)
+      forest.java <- .jcall(forestTrainer, makeResponse(.class_OfflineForest), "trainSerialOnDisk", initial.forest.optional)
     }
     
-    # Need to now load forest trees back into memory
-    forest.java <- .jcall(.class_DataUtils, makeResponse(.class_Forest), "loadForest", savePath, forest$params$forestResponseCombiner$javaObject)
-    
+    if(forest.output[1] == "online"){
+      forest.java <- convertToOnlineForest.Java(forest.java)
+    }
     
   }
   else{ # save directly into memory
     if(cores > 1){
-      forest.java <- .jcall(forestTrainer, makeResponse(.class_Forest), "trainParallelInMemory", initial.forest.optional, as.integer(cores))
+      forest.java <- .jcall(forestTrainer, makeResponse(.class_OnlineForest), "trainParallelInMemory", initial.forest.optional, as.integer(cores))
     } else {
-      forest.java <- .jcall(forestTrainer, makeResponse(.class_Forest), "trainSerialInMemory", initial.forest.optional)
+      forest.java <- .jcall(forestTrainer, makeResponse(.class_OnlineForest), "trainSerialInMemory", initial.forest.optional)
     }
   }
   

R/convertToOnlineForest.R

@@ -0,0 +1,38 @@
+
+#' Convert to Online Forest
+#'
+#' Some forests are too large to store in memory and have been saved to disk.
+#' They can still be used, but their performance is much slower. If there's
+#' enough memory, they can easily be converted into an in-memory forest that is
+#' faster to use.
+#'
+#' @param forest The offline forest.
+#'
+#' @return An online, in memory forst.
+#' @export
+#' 
+convertToOnlineForest <- function(forest){
+  old.forest.object <- forest$javaObject
+  
+  if(getJavaClass(old.forest.object) == "ca.joeltherrien.randomforest.tree.OnlineForest"){
+    
+    warning("forest is already in-memory")
+    return(forest)
+    
+  } else if(getJavaClass(old.forest.object) == "ca.joeltherrien.randomforest.tree.OfflineForest"){
+    
+    forest$javaObject <- convertToOnlineForest.Java(old.forest.object)
+    return(forest)
+    
+  } else{
+    stop("'forest' is not an online or offline forest")
+  }
+  
+}
+
+# Internal function
+convertToOnlineForest.Java <- function(forest.java){
+  offline.forest <- .jcast(forest.java, .class_OfflineForest)
+  online.forest <- .jcall(offline.forest, makeResponse(.class_OnlineForest), "createOnlineCopy")
+  return(online.forest)
+}

R/cr_components.R

@@ -42,7 +42,7 @@ CR_FunctionCombiner <- function(events, times = NULL){
   }
 
   javaObject <- .jnew(.class_CompetingRiskFunctionCombiner, eventArray, timeArray)
-  javaObject <- .jcast(javaObject, .class_ResponseCombiner)
+  javaObject <- .jcast(javaObject, .class_ForestResponseCombiner)
 
   combiner <- list(javaObject=javaObject,
                    call=match.call(),

R/cr_integratedBrierScore.R

@@ -0,0 +1,83 @@
+
+
+#' Integrated Brier Score
+#'
+#' Used to calculate the Integrated Brier Score, which for the competing risks
+#' setting is the integral of the squared difference between each observed
+#' cumulative incidence function (CIF) for each observation and the
+#' corresponding predicted CIF. If the survivor function (1 - CDF) of the
+#' censoring distribution is provided, weights can be calculated to account for
+#' the censoring.
+#'
+#' @return A numeric vector of the Integrated Brier Score for each prediction.
+#' @param responses A list of responses corresponding to the provided
+#'   mortalities; use \code{\link{CR_Response}}.
+#' @param predictions The predictions to be tested against.
+#' @param event The event type for the error to be calculated on.
+#' @param time \code{time} specifies the upper bound of the integral.
+#' @param censoringDistribution Optional; if provided then weights are
+#'   calculated on the errors. There are three ways to provide it - \itemize{
+#'   \item{If you have all the censor times and just want to use a simple
+#'   empirical estimate of the distribution, just provide a numeric vector of
+#'   all of the censor times and it will be automatically calculated.} \item{You
+#'   can directly specify the survivor function by providing a list with two
+#'   numeric vectors called \code{x} and \code{y}. They should be of the same
+#'   length and correspond to each point. It is assumed that previous to the
+#'   first value in \code{y} the \code{y} value is 1.0; and that the function
+#'   you provide is a right-continuous step function.} \item{You can provide a
+#'   function from \code{\link[stats]{stepfun}}. Note that this only supports
+#'   functions where \code{right = FALSE} (default), and that the first y value
+#'   (corresponding to y before the first x value) will be to set to 1.0
+#'   regardless of what is specified.}
+#'
+#'   }
+#' @param parallel A logical indicating whether multiple cores should be
+#'   utilized when calculating the error. Available as an option because it's
+#'   been observed that using Java's \code{parallelStream} can be unstable on
+#'   some systems. Default value is \code{TRUE}; only set to \code{FALSE} if you
+#'   get strange errors while predicting.
+#'
+#' @export
+#' @references Section 4.2 of Ishwaran H, Gerds TA, Kogalur UB, Moore RD, Gange
+#'   SJ, Lau BM (2014). “Random Survival Forests for Competing Risks.”
+#'   Biostatistics, 15(4), 757–773. doi:10.1093/ biostatistics/kxu010.
+#'
+#' @examples
+#' data <- data.frame(delta=c(1,1,0,0,2,2), T=1:6, x=1:6)
+#'
+#' model <- train(CR_Response(delta, T) ~ x, data, ntree=100, numberOfSplits=0, mtry=1, nodeSize=1)
+#'
+#' newData <- data.frame(delta=c(1,0,2,1,0,2), T=1:6, x=1:6)
+#' predictions <- predict(model, newData)
+#'
+#' scores <- integratedBrierScore(CR_Response(data$delta, data$T), predictions, 1, 6.0)
+#' 
+integratedBrierScore <- function(responses, predictions, event, time, censoringDistribution = NULL, parallel = TRUE){
+  if(length(responses$eventTime) != length(predictions)){
+    stop("Length of responses and predictions must be equal.")
+  } 
+  
+  java.censoringDistribution <- NULL
+  if(!is.null(censoringDistribution)){
+    java.censoringDistribution <- processCensoringDistribution(censoringDistribution)
+    java.censoringDistribution <- .object_Optional(java.censoringDistribution)
+  }
+  else{
+    java.censoringDistribution <- .object_Optional(NULL)
+  }
+  
+  
+  predictions.java <- lapply(predictions, function(x){return(x$javaObject)})
+  predictions.java <- convertRListToJava(predictions.java)
+  
+  errors <- .jcall(.class_CompetingRiskUtils, "[D", "calculateIBSError", 
+         responses$javaObject, 
+         predictions.java, 
+         java.censoringDistribution,
+         as.integer(event), 
+         time,
+         parallel)
+  
+  return(errors)
+  
+}

R/cr_naiveConcordance.R

@@ -16,6 +16,11 @@
 #'   list should correspond to one of the events in the order of event 1 to J,
 #'   and should be a vector of the same length as responses.
 #' @export
+#' @references Section 3.2 of Wolbers, Marcel, Paul Blanche, Michael T. Koller,
+#'   Jacqueline C M Witteman, and Thomas A Gerds. 2014. “Concordance for
+#'   Prognostic Models with Competing Risks.” Biostatistics 15 (3): 526–39.
+#'   https://doi.org/10.1093/biostatistics/kxt059.
+#'
 #' @examples
 #' data <- data.frame(delta=c(1,1,0,0,2,2), T=1:6, x=1:6)
 #'

R/createRightContinuousStepFunction.R

@@ -0,0 +1,11 @@
+# Internal function
+createRightContinuousStepFunction <- function(x, y, defaultY){
+  x.java <- .jarray(as.numeric(x))
+  y.java <- .jarray(as.numeric(y))
+  
+  # as.numeric is explicitly required in case integers were accidently passed
+  # in.
+  newFun <- .jnew(.class_RightContinuousStepFunction, as.numeric(x), as.numeric(y), as.numeric(defaultY))
+  return(newFun)
+  
+}

R/create_java_covariates.R

@@ -29,8 +29,8 @@
 NULL
 
 # @rdname covariates
-Java_BooleanCovariate <- function(name, index){
-    covariate <- .jnew(.class_BooleanCovariate, name, as.integer(index))
+Java_BooleanCovariate <- function(name, index, na.penalty){
+    covariate <- .jnew(.class_BooleanCovariate, name, as.integer(index), na.penalty)
     covariate <- .jcast(covariate, .class_Object) # needed for later adding it into Java Lists
 
     return(covariate)
@@ -38,19 +38,19 @@ Java_BooleanCovariate <- function(name, index){
 
 # @rdname covariates
 # @param levels The levels of the factor as a character vector
-Java_FactorCovariate <- function(name, index, levels){
+Java_FactorCovariate <- function(name, index, levels, na.penalty){
     levelsArray <- .jarray(levels, makeResponse(.class_String))
     levelsList <- .jcall("java/util/Arrays", "Ljava/util/List;", "asList", .jcast(levelsArray, "[Ljava/lang/Object;"))
 
-    covariate <- .jnew(.class_FactorCovariate, name, as.integer(index), levelsList)
+    covariate <- .jnew(.class_FactorCovariate, name, as.integer(index), levelsList, na.penalty)
     covariate <- .jcast(covariate, .class_Object) # needed for later adding it into Java Lists
 
     return(covariate)
 }
 
 # @rdname covariates
-Java_NumericCovariate <- function(name, index){
-    covariate <- .jnew(.class_NumericCovariate, name, as.integer(index))
+Java_NumericCovariate <- function(name, index, na.penalty){
+    covariate <- .jnew(.class_NumericCovariate, name, as.integer(index), na.penalty)
     covariate <- .jcast(covariate, .class_Object) # needed for later adding it into Java Lists
 
     return(covariate)

R/java_classes_directory.R

@@ -10,15 +10,20 @@
 .class_Collection <- "java/util/Collection"
 .class_Serializable <- "java/io/Serializable"
 .class_File <- "java/io/File"
+.class_Random <- "java/util/Random"
+.class_Class <- "java/lang/Class"
 
 # Utility Classes
 .class_DataUtils <- "ca/joeltherrien/randomforest/utils/DataUtils"
 .class_RUtils <- "ca/joeltherrien/randomforest/utils/RUtils"
+.class_Utils <- "ca/joeltherrien/randomforest/utils/Utils"
 .class_CompetingRiskUtils <- "ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskUtils"
 .class_Settings <- "ca/joeltherrien/randomforest/Settings"
 
 # Misc. Classes
 .class_RightContinuousStepFunction <- "ca/joeltherrien/randomforest/utils/RightContinuousStepFunction"
+.class_CompetingRiskResponse <- "ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskResponse"
+.class_CompetingRiskResponseWithCensorTime <- "ca/joeltherrien/randomforest/responses/competingrisk/CompetingRiskResponseWithCensorTime"
 
  # TreeTrainer & its Builder
 .class_TreeTrainer <- "ca/joeltherrien/randomforest/tree/TreeTrainer"
@@ -37,9 +42,12 @@
 
 # Forest class
 .class_Forest <- "ca/joeltherrien/randomforest/tree/Forest"
+.class_OnlineForest <- "ca/joeltherrien/randomforest/tree/OnlineForest"
+.class_OfflineForest <- "ca/joeltherrien/randomforest/tree/OfflineForest"
 
 # ResponseCombiner classes
 .class_ResponseCombiner <- "ca/joeltherrien/randomforest/tree/ResponseCombiner"
+.class_ForestResponseCombiner <- "ca/joeltherrien/randomforest/tree/ForestResponseCombiner"
 .class_CompetingRiskResponseCombiner <- "ca/joeltherrien/randomforest/responses/competingrisk/combiner/CompetingRiskResponseCombiner"
 .class_CompetingRiskFunctionCombiner <- "ca/joeltherrien/randomforest/responses/competingrisk/combiner/CompetingRiskFunctionCombiner"
 .class_MeanResponseCombiner <- "ca/joeltherrien/randomforest/responses/regression/MeanResponseCombiner"
@@ -50,12 +58,20 @@
 .class_LogRankSplitFinder <- "ca/joeltherrien/randomforest/responses/competingrisk/splitfinder/LogRankSplitFinder"
 .class_WeightedVarianceSplitFinder <- "ca/joeltherrien/randomforest/responses/regression/WeightedVarianceSplitFinder"
 
-.object_Optional <- function(forest=NULL){
-  if(is.null(forest)){
+# VIMP classes
+.class_IBSCalculator <- "ca/joeltherrien/randomforest/responses/competingrisk/IBSCalculator"
+.class_ErrorCalculator <- "ca/joeltherrien/randomforest/tree/vimp/ErrorCalculator"
+.class_RegressionErrorCalculator <- "ca/joeltherrien/randomforest/tree/vimp/RegressionErrorCalculator"
+.class_IBSErrorCalculatorWrapper <- "ca/joeltherrien/randomforest/tree/vimp/IBSErrorCalculatorWrapper"
+.class_VariableImportanceCalculator <- "ca/joeltherrien/randomforest/tree/vimp/VariableImportanceCalculator"
+
+
+.object_Optional <- function(object=NULL){
+  if(is.null(object)){
     return(.jcall("java/util/Optional", "Ljava/util/Optional;", "empty"))
   } else{
-    forest <- .jcast(forest, .class_Object)
-    return(.jcall("java/util/Optional", "Ljava/util/Optional;", "of", forest))
+    object <- .jcast(object, .class_Object)
+    return(.jcall("java/util/Optional", "Ljava/util/Optional;", "of", object))
   }
   
 }
@@ -66,3 +82,9 @@
 makeResponse <- function(className){
   return(paste0("L", className, ";"))
 }
+
+getJavaClass <- function(object){
+  class <- .jcall(object, makeResponse(.class_Class), "getClass")
+  className <- .jcall(class, "S", "getName")
+  return(className)
+}

R/loadData.R

@@ -1,4 +1,4 @@
-loadData <- function(data, xVarNames, responses, covariateList.java = NULL){
+loadData <- function(data, xVarNames, responses, covariateList.java = NULL, na.penalty = NULL){
 
   if(class(responses) == "integer" | class(responses) == "numeric"){
     responses <- Numeric(responses)
@@ -6,7 +6,7 @@ loadData <- function(data, xVarNames, responses, covariateList.java = NULL){
 
   # connectToData provides a pre-created covariate list we can re-use
   if(is.null(covariateList.java)){
-    covariateList.java <- getCovariateList(data, xVarNames)
+    covariateList.java <- getCovariateList(data, xVarNames, na.penalty)
   }
   
   textColumns <- list()
@@ -18,11 +18,11 @@ loadData <- function(data, xVarNames, responses, covariateList.java = NULL){
   rowList <- .jcall(.class_RUtils, makeResponse(.class_List), "importDataWithResponses",
                     responses$javaObject, covariateList.java, textData)
 
-  return(list(covariateList=covariateList.java, dataset=rowList))
+  return(list(covariateList = covariateList.java, dataset = rowList, responses = responses))
 
 }
 
-getCovariateList <- function(data, xvarNames){
+getCovariateList <- function(data, xvarNames, na.penalty){
   covariateList <- .jcast(.jnew(.class_ArrayList, length(xvarNames)), .class_List)
 
   for(i in 1:length(xvarNames)){
@@ -31,14 +31,14 @@ getCovariateList <- function(data, xvarNames){
     column <- data[,xName]
 
     if(class(column) == "numeric" | class(column) == "integer"){
-      covariate <- Java_NumericCovariate(xName, i-1)
+      covariate <- Java_NumericCovariate(xName, i-1, na.penalty[i])
     }
     else if(class(column) == "logical"){
-      covariate <- Java_BooleanCovariate(xName, i-1)
+      covariate <- Java_BooleanCovariate(xName, i-1, na.penalty[i])
     }
     else if(class(column) == "factor"){
       lvls <- levels(column)
-      covariate <- Java_FactorCovariate(xName, i-1, lvls)
+      covariate <- Java_FactorCovariate(xName, i-1, lvls, na.penalty[i])
     }
     else{
       stop("Unknown column type")
@@ -54,15 +54,7 @@ getCovariateList <- function(data, xvarNames){
 
 loadPredictionData <- function(newData, covariateList.java){
   
-  xVarNames <- character(.jcall(covariateList.java, "I", "size"))
-  for(j in 1:length(xVarNames)){
-    covariate.java <- .jcast(
-      .jcall(covariateList.java, makeResponse(.class_Object), "get", as.integer(j-1)),
-      .class_Covariate
-    )
-    
-    xVarNames[j] <- .jcall(covariate.java, makeResponse(.class_String), "getName")
-  }
+  xVarNames <- extractCovariateNamesFromJavaList(covariateList.java)
   
   if(any(!(xVarNames %in% names(newData)))){
     varsMissing = xVarNames[!(xVarNames %in% names(newData))]
@@ -84,3 +76,16 @@ loadPredictionData <- function(newData, covariateList.java){
   return(rowList)
 }
 
+extractCovariateNamesFromJavaList <- function(covariateList.java){
+  xVarNames <- character(.jcall(covariateList.java, "I", "size"))
+  for(j in 1:length(xVarNames)){
+    covariate.java <- .jcast(
+      .jcall(covariateList.java, makeResponse(.class_Object), "get", as.integer(j-1)),
+      .class_Covariate
+    )
+    
+    xVarNames[j] <- .jcall(covariate.java, makeResponse(.class_String), "getName")
+  }
+  
+  return(xVarNames)
+}

R/loadForest.R

@@ -5,6 +5,12 @@
 #' Loads a random forest that was saved using \code{\link{saveForest}}.
 #'
 #' @param directory The directory created that saved the previous forest.
+#' @param forest.output Specifies whether the forest loaded should be loaded
+#'   into memory, or reflect the saved files where only one tree is loaded at a
+#'   time.
+#' @param maxTreeNum If for some reason you only want to load the number of
+#'   trees up until a certain point, you can specify maxTreeNum as a single
+#'   number.
 #' @return A JForest object; see \code{\link{train}} for details.
 #' @export
 #' @seealso \code{\link{train}}, \code{\link{saveForest}}
@@ -20,7 +26,11 @@
 #'
 #' saveForest(forest, "trees")
 #' new_forest <- loadForest("trees")
-loadForest <- function(directory){
+loadForest <- function(directory, forest.output = c("online", "offline"), maxTreeNum = NULL){
+  
+  if(is.null(forest.output) | length(forest.output)==0 | !(forest.output[1] %in% c("online", "offline"))){
+    stop("forest.output must be one of c(\"online\", \"offline\")")
+  }
   
   # First load the response combiners and the split finders
   nodeResponseCombiner.java <- .jcall(.class_DataUtils, makeResponse(.class_Object), "loadObject", paste0(directory, "/nodeResponseCombiner.jData"))
@@ -30,7 +40,7 @@ loadForest <- function(directory){
   splitFinder.java <- .jcast(splitFinder.java, .class_SplitFinder)
   
   forestResponseCombiner.java <- .jcall(.class_DataUtils, makeResponse(.class_Object), "loadObject", paste0(directory, "/forestResponseCombiner.jData"))
-  forestResponseCombiner.java <- .jcast(forestResponseCombiner.java, .class_ResponseCombiner)
+  forestResponseCombiner.java <- .jcast(forestResponseCombiner.java, .class_ForestResponseCombiner)
   
   covariateList <- .jcall(.class_DataUtils, makeResponse(.class_Object), "loadObject", paste0(directory, "/covariateList.jData"))
   covariateList <- .jcast(covariateList, .class_List)
@@ -42,8 +52,11 @@ loadForest <- function(directory){
   params$splitFinder$javaObject <- splitFinder.java
   params$forestResponseCombiner$javaObject <- forestResponseCombiner.java
   
-  forest <- loadForestArgumentsSpecified(directory, params$nodeResponseCombiner, params$splitFinder, params$forestResponseCombiner, covariateList, call,
-                                      params$ntree, params$numberOfSplits, params$mtry, params$nodeSize, params$maxNodeDepth, params$splitPureNodes, params$randomSeed)
+  forest <- loadForestArgumentsSpecified(directory, params$nodeResponseCombiner, params$splitFinder, 
+                                         params$forestResponseCombiner, covariateList, call,
+                                         params$ntree, params$numberOfSplits, params$mtry,
+                                         params$nodeSize, params$maxNodeDepth, params$splitPureNodes,
+                                         params$randomSeed, forest.output, maxTreeNum)
   
   return(forest)
   
@@ -55,8 +68,11 @@ loadForest <- function(directory){
 # that uses the Java version's settings yaml file to recreate the forest, but
 # I'd appreciate knowing that someone's going to use it first (email me; see
 # README).
-loadForestArgumentsSpecified <- function(treeDirectory, nodeResponseCombiner, splitFinder, forestResponseCombiner, 
-                                 covariateList.java, call, ntree, numberOfSplits, mtry, nodeSize, maxNodeDepth = 100000, splitPureNodes=TRUE, randomSeed=NULL){
+loadForestArgumentsSpecified <- function(treeDirectory, nodeResponseCombiner, splitFinder,
+                                         forestResponseCombiner, covariateList.java, call,
+                                         ntree, numberOfSplits, mtry, nodeSize,
+                                         maxNodeDepth = 100000, splitPureNodes=TRUE,
+                                         randomSeed=NULL, forest.output = "online", maxTreeNum = NULL){
   
   params <- list(
     splitFinder=splitFinder,
@@ -71,7 +87,33 @@ loadForestArgumentsSpecified <- function(treeDirectory, nodeResponseCombiner, sp
     randomSeed=randomSeed
   )
   
-  forest.java <- .jcall(.class_DataUtils, makeResponse(.class_Forest), "loadForest", treeDirectory, forestResponseCombiner$javaObject)
+  forest.java <- NULL
+  if(forest.output[1] == "online"){
+    castedForestResponseCombiner <- .jcast(forestResponseCombiner$javaObject, .class_ResponseCombiner) # OnlineForest constructor takes a ResponseCombiner
+    
+    if(is.null(maxTreeNum)){
+      forest.java <- .jcall(.class_DataUtils, makeResponse(.class_OnlineForest), "loadOnlineForest",
+                            treeDirectory, castedForestResponseCombiner)
+    } else{
+      tree.file.array <- .jcall(.class_RUtils, paste0("[", makeResponse(.class_File)), "getTreeFileArray", 
+                                treeDirectory, as.integer(maxTreeNum), evalArray = FALSE)
+      forest.java <- .jcall(.class_DataUtils, makeResponse(.class_OnlineForest), "loadOnlineForest",
+                            tree.file.array, castedForestResponseCombiner)
+      
+    }
+    
+  } else{ # offline forest
+    if(is.null(maxTreeNum)){
+      path.as.file <- .jnew(.class_File, treeDirectory)
+      forest.java <- .jnew(.class_OfflineForest, path.as.file, forestResponseCombiner$javaObject)
+    } else{
+      tree.file.array <- .jcall(.class_RUtils, paste0("[", makeResponse(.class_File)), "getTreeFileArray", 
+                                treeDirectory, as.integer(maxTreeNum), evalArray = FALSE)
+      forest.java <- .jnew(.class_OfflineForest, tree.file.array, forestResponseCombiner$javaObject)
+    }
+  }
+  
+  
   
   forestObject <- list(call=call, javaObject=forest.java, covariateList=covariateList.java, params=params)
   class(forestObject) <- "JRandomForest"

R/misc.R

@@ -14,6 +14,23 @@ convertRListToJava <- function(lst){
   return(javaList)
 }
 
+#Internal function
+convertJavaListToR <- function(javaList, class = .class_Object){
+  lst <- list()
+  
+  javaList.length <- .jcall(javaList, "I", "size")
+  
+  for(i in 0:(javaList.length - 1)){
+    object <- .jcall(javaList, makeResponse(.class_Object), "get", as.integer(i))
+    object <- .jcast(object, class)
+    
+    lst[[i+1]] <- object
+  }
+  
+  return(lst)
+  
+}
+
 #' @export
 print.SplitFinder = function(x, ...) print(x$call)
 

R/predict.R

@@ -99,7 +99,7 @@ predict.JRandomForest <- function(object, newData=NULL, parallel=TRUE, out.of.ba
   predictionsJava <- .jcall(forestObject, makeResponse(.class_List), function.to.use, predictionDataList)
 
   if(predictionClass == "numeric"){
-    predictions <- vector(length=nrow(newData), mode="numeric")
+    predictions <- vector(length=numRows, mode="numeric")
   }
   else{
     predictions <- list()

R/processCensoringDistribution.R

@@ -0,0 +1,39 @@
+
+
+# Internal function. Takes a censoring distribution and turns it into a
+# RightContinuousStepFunction Java object.
+processCensoringDistribution <- function(censoringDistribution){
+
+  if(is.numeric(censoringDistribution)){
+    # estimate ECDF
+    censoringTimes <- .jarray(censoringDistribution, "D")
+    java.censoringDistribution <- .jcall(.class_Utils, makeResponse(.class_RightContinuousStepFunction), "estimateOneMinusECDF", censoringTimes)
+    
+  } else if(is.list(censoringDistribution)){
+    # First check that censoringDistribution fits the correct format
+    if(is.null(censoringDistribution$x) | is.null(censoringDistribution$y)){
+      stop("If the censoringDistribution is provided as a list, it must have an x and a y item that are numeric.")
+    }
+    
+    if(length(censoringDistribution$x) != length(censoringDistribution$y)){
+      stop("x and y in censoringDistribution must have the same length.")
+    }
+    
+    if(!is.numeric(censoringDistribution$x) | !is.numeric(censoringDistribution$y)){
+      stop("x and y in censoringDistribution must both be numeric.")
+    }
+    
+    java.censoringDistribution <- createRightContinuousStepFunction(censoringDistribution$x, censoringDistribution$y, defaultY = 1.0)
+    
+  } else if("stepfun" %in% class(censoringDistribution)){
+    x <- stats::knots(censoringDistribution)
+    y <- censoringDistribution(x)
+    
+    java.censoringDistribution <- createRightContinuousStepFunction(x, y, defaultY = 1.0)
+  }
+  else{
+    stop("Invalid censoringDistribution")
+  }
+    
+  return(java.censoringDistribution)
+}

R/processFormula.R

@@ -0,0 +1,95 @@
+
+# Internal function that takes a formula and processes it for use in the Java
+# code. existingCovariateList is optional; if not provided then a new one is
+# created internally.
+processFormula <- function(formula, data, covariateList.java = NULL, na.penalty = NULL){
+  
+  # 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
+  # after being imported into Java
+  if(class(data) == "environment"){
+    if(is.null(data$data)){
+      stop("When providing an environment with the dataset, the environment must contain an item called 'data'")
+    }
+    
+    env <- data
+    data <- env$data
+    env$data <- NULL
+    gc()
+  }
+  
+  yVar <- formula[[2]]
+  
+  responses <- NULL
+  variablesToDrop <- character(0)
+  
+  # yVar is a call object; as.character(yVar) will be the different components, including the parameters.
+  # if the length of yVar is > 1 then it's a function call. If the length is 1, and it's not in data, 
+  # then we also need to explicitly evaluate it
+  if(class(yVar) == "call" || !(as.character(yVar) %in% colnames(data))){
+    # yVar is a function like CR_Response
+    responses <- eval(expr=yVar, envir=data)
+    
+    if(class(formula[[3]]) == "name" && as.character(formula[[3]])=="."){
+      # do any of the variables match data in data? We need to track that so we can drop them later
+      variablesToDrop <- as.character(yVar)[as.character(yVar) %in% names(data)]
+    }
+    
+    formula[[2]] <- NULL
+    
+  } else if(class(yVar) == "name"){ # and implicitly yVar is contained in data
+    variablesToDrop <- as.character(yVar)
+  }
+  
+  # Includes responses which we may need to later cut out if `.` was used on the
+  # right-hand-side
+  filteredData <- stats::model.frame(formula=formula, data=data, na.action=stats::na.pass)
+  
+  if(is.null(responses)){ # If this if-statement runs then we have a simple (i.e. numeric) response
+    responses <- stats::model.response(filteredData)
+  }
+  
+  # remove any response variables on the right-hand-side
+  covariateData <- filteredData[, !(names(filteredData) %in% variablesToDrop), drop=FALSE]
+  
+  # Now that we know how many predictor variables we have, we should check na.penalty
+  if(!is.null(na.penalty)){
+    if(!is.numeric(na.penalty) & !is.logical(na.penalty)){
+      stop("na.penalty must be either logical or numeric.")
+    }
+    
+    if(is.logical(na.penalty) & length(na.penalty) != 1 & length(na.penalty) != ncol(covariateData)){
+      stop("na.penalty must have length of either 1 or the number of predictor variables if logical.")
+    }
+    
+    if(is.numeric(na.penalty) & length(na.penalty) != 1){
+      stop("na.penalty must have length 1 if logical.")
+    }
+    
+    if(anyNA(na.penalty)){
+      stop("na.penalty cannot contain NAs.")
+    }
+
+    
+    # All good; now to transform it.
+    if(is.numeric(na.penalty)){
+      na.threshold <- na.penalty
+      na.penalty <- apply(covariateData, 2, function(x){mean(is.na(x))}) >= na.threshold
+    }
+    else if(is.logical(na.penalty) & length(na.penalty) == 1){
+      na.penalty <- rep(na.penalty, times = ncol(covariateData))
+    }
+    # else{} - na.penalty is logical and the correct length; no need to do anything to it
+    
+  }
+  
+  dataset <- loadData(
+    covariateData, 
+    colnames(covariateData), 
+    responses, 
+    covariateList.java = covariateList.java,
+    na.penalty = na.penalty
+  )
+  
+  return(dataset)
+}

R/regressionComponents.R

@@ -48,7 +48,7 @@ WeightedVarianceSplitFinder <- function(){
 #'
 MeanResponseCombiner <- function(){
   javaObject <- .jnew(.class_MeanResponseCombiner)
-  javaObject <- .jcast(javaObject, .class_ResponseCombiner)
+  javaObject <- .jcast(javaObject, .class_ForestResponseCombiner)
 
   combiner <- list(javaObject=javaObject, call=match.call(), outputClass="numeric")
   combiner$convertToRFunction <- function(javaObject, ...){

R/train.R

@@ -14,11 +14,11 @@ getCores <- function(){
   return(cores)
 }
 
-train.internal <- function(responses, covariateData, splitFinder, 
+train.internal <- function(dataset, splitFinder, 
                            nodeResponseCombiner, forestResponseCombiner, ntree, 
                            numberOfSplits, mtry, nodeSize, maxNodeDepth, 
                            splitPureNodes, savePath, savePath.overwrite, 
-                           cores, randomSeed, displayProgress){
+                           forest.output, cores, randomSeed, displayProgress){
   
   # Some quick checks on parameters
   ntree <- as.integer(ntree)
@@ -51,16 +51,20 @@ train.internal <- function(responses, covariateData, splitFinder,
     stop("savePath.overwrite must be one of c(\"warn\", \"delete\", \"merge\")")
   }
   
+  if(is.null(forest.output) | length(forest.output)==0 | !(forest.output[1] %in% c("online", "offline"))){
+    stop("forest.output must be one of c(\"online\", \"offline\")")
+  }
+  
   if(is.null(splitFinder)){
-    splitFinder <- splitFinderDefault(responses)
+    splitFinder <- splitFinderDefault(dataset$responses)
   }
   
   if(is.null(nodeResponseCombiner)){
-    nodeResponseCombiner <- nodeResponseCombinerDefault(responses)
+    nodeResponseCombiner <- nodeResponseCombinerDefault(dataset$responses)
   }
   
   if(is.null(forestResponseCombiner)){
-    forestResponseCombiner <- forestResponseCombinerDefault(responses)
+    forestResponseCombiner <- forestResponseCombinerDefault(dataset$responses)
   }
   
 
@@ -75,20 +79,6 @@ train.internal <- function(responses, covariateData, splitFinder,
     stop("forestResponseCombiner must be a ResponseCombiner")
   }
   
-  if(class(covariateData)=="environment"){
-    if(is.null(covariateData$data)){
-      stop("When providing an environment with the dataset, the environment must contain an item called 'data'")
-    }
-    dataset <- loadData(covariateData$data, colnames(covariateData$data), responses)
-    covariateData$data <- NULL # save memory, hopefully
-    gc() # explicitly try to save memory
-  }
-  else{
-    dataset <- loadData(covariateData, colnames(covariateData), responses)
-  }
-
-  
-  
   treeTrainer <- createTreeTrainer(responseCombiner=nodeResponseCombiner,
                                    splitFinder=splitFinder,
                                    covariateList=dataset$covariateList,
@@ -143,22 +133,23 @@ train.internal <- function(responses, covariateData, splitFinder,
                          params=params,
                          forestCall=match.call())
     
+    forest.java <- NULL
     if(cores > 1){
-      .jcall(forestTrainer, "V", "trainParallelOnDisk", .object_Optional(), as.integer(cores))
+      forest.java <- .jcall(forestTrainer, makeResponse(.class_OfflineForest), "trainParallelOnDisk", .object_Optional(), as.integer(cores))
     } else {
-      .jcall(forestTrainer, "V", "trainSerialOnDisk", .object_Optional())
+      forest.java <- .jcall(forestTrainer, makeResponse(.class_OfflineForest), "trainSerialOnDisk", .object_Optional())
     }
     
-    # Need to now load forest trees back into memory
-    forest.java <- .jcall(.class_DataUtils, makeResponse(.class_Forest), "loadForest", savePath, forestResponseCombiner$javaObject)
-    
+    if(forest.output[1] == "online"){
+      forest.java <- convertToOnlineForest.Java(forest.java)
+    }
 
   }
   else{ # save directly into memory
     if(cores > 1){
-      forest.java <- .jcall(forestTrainer, makeResponse(.class_Forest), "trainParallelInMemory", .object_Optional(), as.integer(cores))
+      forest.java <- .jcall(forestTrainer, makeResponse(.class_OnlineForest), "trainParallelInMemory", .object_Optional(), as.integer(cores))
     } else {
-      forest.java <- .jcall(forestTrainer, makeResponse(.class_Forest), "trainSerialInMemory", .object_Optional())
+      forest.java <- .jcall(forestTrainer, makeResponse(.class_OnlineForest), "trainSerialInMemory", .object_Optional())
     }
   }
   
@@ -188,13 +179,13 @@ train.internal <- function(responses, covariateData, splitFinder,
 #'
 #' @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}
+#'   constructed.
 #' @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
+#'   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
@@ -202,19 +193,19 @@ train.internal <- function(responses, covariateData, splitFinder,
 #' @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}}.
+#'   \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}}.
+#'   \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
@@ -231,6 +222,20 @@ train.internal <- function(responses, covariateData, splitFinder,
 #' @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 na.penalty This parameter controls whether predictor variables with
+#'   NAs should be penalized when being considered for a best split. Best splits
+#'   (and the associated score) are determined on only non-NA data; the penalty
+#'   is to take the best split identified, and to randomly assign any NAs
+#'   (according to the proportion of data split left and right), and then
+#'   recalculate the corresponding split score, when is then compared with the
+#'   other split candiate variables. This penalty adds some computational time,
+#'   so it may be disabled for some variables. \code{na.penalty} may be
+#'   specified as a vector of logicals indicating, for each predictor variable,
+#'   whether the penalty should be applied to that variable. If it's length 1
+#'   then it applies to all variables. Alternatively, a single numeric value may
+#'   be provided to indicate a threshold whereby the penalty is activated only
+#'   if the proportion of NAs for that variable in the training set exceeds that
+#'   threshold.
 #' @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
@@ -253,6 +258,10 @@ train.internal <- function(responses, covariateData, splitFinder,
 #'   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 forest.output This parameter only applies if \code{savePath} has been
+#'   set; set to 'online' (default) and the saved forest will be loaded into
+#'   memory after being trained. Set to 'offline' and the forest is not saved
+#'   into memory, but can still be used in a memory unintensive manner.
 #' @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
@@ -310,78 +319,21 @@ train.internal <- function(responses, covariateData, splitFinder,
 #' 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){
+                  nodeSize, maxNodeDepth = 100000, na.penalty = TRUE, splitPureNodes=TRUE, 
+                  savePath = NULL, savePath.overwrite = c("warn", "delete", "merge"), 
+                  forest.output = c("online", "offline"),
+                  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
-  # after being imported into Java
-  env <- NULL
-  if(class(data) == "environment"){
-    if(is.null(data$data)){
-      stop("When providing an environment with the dataset, the environment must contain an item called 'data'")
-    }
+  dataset <- processFormula(formula, data, na.penalty = na.penalty)
   
-    env <- data
-    data <- env$data
-  }
-
-  yVar <- formula[[2]]
-
-  responses <- NULL
-  variablesToDrop <- character(0)
-  
-  # yVar is a call object; as.character(yVar) will be the different components, including the parameters.
-  # if the length of yVar is > 1 then it's a function call. If the length is 1, and it's not in data, 
-  # then we also need to explicitly evaluate it
-  if(class(yVar)=="call" || !(as.character(yVar) %in% colnames(data))){
-    # yVar is a function like CompetingRiskResponses
-    responses <- eval(expr=yVar, envir=data)
-    
-    if(class(formula[[3]]) == "name" && as.character(formula[[3]])=="."){
-      # do any of the variables match data in data? We need to track that so we can drop them later
-      variablesToDrop <- as.character(yVar)[as.character(yVar) %in% names(data)]
-    }
-    
-    formula[[2]] <- NULL
-
-  } else if(class(yVar)=="name"){ # and implicitly yVar is contained in data
-    variablesToDrop <- as.character(yVar)
-  }
-
-  # Includes responses which we may need to later cut out
-  mf <- stats::model.frame(formula=formula, data=data, na.action=stats::na.pass)
-
-  if(is.null(responses)){
-    responses <- stats::model.response(mf)
-  }
-
-  # remove any response variables
-  mf <- mf[,!(names(mf) %in% variablesToDrop), drop=FALSE]
-  
-  # If environment was provided instead of data
-  if(!is.null(env)){
-    env$data <- mf
-    rm(data)
-    forest <- train.internal(responses, env, splitFinder = splitFinder,
+  forest <- train.internal(dataset, 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.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)
-  }
+                           savePath.overwrite = savePath.overwrite, forest.output = forest.output,
+                           cores = cores, randomSeed = randomSeed, displayProgress = displayProgress)
   
   forest$call <- match.call()
   forest$formula <- formula
@@ -429,7 +381,9 @@ createTreeTrainer <- function(responseCombiner, splitFinder, covariateList, numb
 
   builder <- .jcall(.class_TreeTrainer, builderClassReturned, "builder")
   
-  builder <- .jcall(builder, builderClassReturned, "responseCombiner", responseCombiner$javaObject)
+  responseCombinerCasted <- .jcast(responseCombiner$javaObject, .class_ResponseCombiner) # might need to cast a ForestResponseCombiner down
+  
+  builder <- .jcall(builder, builderClassReturned, "responseCombiner", responseCombinerCasted)
   builder <- .jcall(builder, builderClassReturned, "splitFinder", splitFinder$javaObject)
   builder <- .jcall(builder, builderClassReturned, "covariates", covariateList)
   builder <- .jcall(builder, builderClassReturned, "numberOfSplits", as.integer(numberOfSplits))

R/vimp.R

@@ -0,0 +1,175 @@
+
+
+#' Variable Importance
+#'
+#' Calculate variable importance by recording the increase in error when a given
+#' predictor is randomly permuted. Regression forests uses mean squared error;
+#' competing risks uses integrated Brier score.
+#'
+#' @param forest The forest that was trained.
+#' @param newData A test set of the data if available. If not, then out of bag
+#'   errors will be attempted on the training set.
+#' @param randomSeed The source of randomness used to permute the values. Can be
+#'   left blank.
+#' @param events If using competing risks forest, the events that the error
+#'   measure used for VIMP should be calculated on.
+#' @param time If using competing risks forest, the upper bound of the
+#'   integrated Brier score.
+#' @param censoringDistribution (Optional) If using competing risks forest, the
+#'   censoring distribution. See \code{\link{integratedBrierScore} for details.}
+#' @param eventWeights (Optional) If using competing risks forest, weights to be
+#'   applied to the error for each of the \code{events}.
+#'
+#' @return A named numeric vector of importance values.
+#' @export
+#'
+#' @examples
+#' data(wihs)
+#'
+#' forest <- train(CR_Response(status, time) ~ ., wihs,
+#'  ntree = 100, numberOfSplits = 0, mtry=3, nodeSize = 5)
+#'
+#' vimp(forest, events = 1:2, time = 8.0)
+#' 
+vimp <- function(
+  forest,
+  newData = NULL,
+  randomSeed = NULL,
+  type = c("mean", "z", "raw"),
+  events = NULL,
+  time = NULL,
+  censoringDistribution = NULL,
+  eventWeights = NULL){
+  
+  if(is.null(newData) & is.null(forest$dataset)){
+    stop("forest doesn't have a copy of the training data loaded (this happens if you just loaded it); please manually specify newData and possibly out.of.bag")
+  }
+  
+  # Basically we check if type is either null, length 0, or one of the invalid values.
+  # We can't include the last statement in the same statement as length(tyoe) < 1,
+  # because R checks both cases and a different error would display if length(type) == 0
+  typeError = is.null(type) | length(type) < 1
+  if(!typeError){
+    typeError = !(type[1] %in% c("mean", "z", "raw"))
+  }
+  if(typeError){
+    stop("A valid response type must be provided.")
+  }
+  
+  if(is.null(newData)){
+    data.java <- forest$dataset
+    out.of.bag <- TRUE
+    
+  }
+  else{ # newData is provided
+    data.java <- processFormula(forest$formula, newData, forest$covariateList)$dataset
+    out.of.bag <- FALSE
+  }
+  
+  predictionClass <- forest$params$forestResponseCombiner$outputClass
+  
+  if(predictionClass == "CompetingRiskFunctions"){
+    if(is.null(time) | length(time) != 1){
+      stop("time must be set at length 1")
+    }
+    
+    errorCalculator.java <- ibsCalculatorWrapper(
+      events = events,
+      time = time,
+      censoringDistribution = censoringDistribution,
+      eventWeights = eventWeights)
+    
+  } else if(predictionClass == "numeric"){
+    errorCalculator.java <- .jnew(.class_RegressionErrorCalculator)
+    errorCalculator.java <- .jcast(errorCalculator.java, .class_ErrorCalculator)
+    
+  } else{
+    stop(paste0("VIMP not yet supported for ", predictionClass, ". If you're just using a non-custom version of largeRCRF then this is a bug and should be reported."))
+    
+  }
+  
+  forest.trees.java <- .jcall(forest$javaObject, makeResponse(.class_List), "getTrees")
+  
+  vimp.calculator <- .jnew(.class_VariableImportanceCalculator, 
+                           errorCalculator.java, 
+                           forest.trees.java,
+                           data.java,
+                           out.of.bag # isTrainingSet parameter
+                           )
+  
+  random.java <- NULL
+  if(!is.null(randomSeed)){
+    random.java <- .jnew(.class_Random, .jlong(as.integer(randomSeed)))
+  }
+  random.java <- .object_Optional(random.java)
+  
+  covariateRList <- convertJavaListToR(forest$covariateList, class = .class_Covariate)
+  importanceValues <- matrix(nrow = forest$params$ntree, ncol = length(covariateRList))
+  colnames(importanceValues) <- extractCovariateNamesFromJavaList(forest$covariateList)
+  
+  for(j in 1:length(covariateRList)){
+    covariateJava <- covariateRList[[j]]
+    covariateJava <- 
+    
+    importanceValues[, j] <- .jcall(vimp.calculator, "[D", "calculateVariableImportanceRaw", covariateJava, random.java)
+  }
+  
+  if(type[1] == "raw"){
+    return(importanceValues)
+  } else if(type[1] == "mean"){
+    meanImportanceValues <- apply(importanceValues, 2, mean)
+    return(meanImportanceValues)
+  } else if(type[1] == "z"){
+    zImportanceValues <- apply(importanceValues, 2, function(x){
+      meanValue <- mean(x)
+      standardError <- sd(x)/sqrt(length(x))
+      return(meanValue / standardError)
+    })
+    return(zImportanceValues)
+    
+  } else{
+    stop("A valid response type must be provided.")
+  }
+
+  
+  return(importance)
+  
+}
+
+# Internal function
+ibsCalculatorWrapper <- function(events, time, censoringDistribution = NULL, eventWeights = NULL){
+  if(is.null(events)){
+    stop("events must be specified if using vimp on competing risks data")
+  }
+  
+  if(is.null(time)){
+    stop("time must be specified if using vimp on competing risks data")
+  }
+  
+  
+  java.censoringDistribution <- NULL
+  if(!is.null(censoringDistribution)){
+    java.censoringDistribution <- processCensoringDistribution(censoringDistribution)
+    java.censoringDistribution <- .object_Optional(java.censoringDistribution)
+  }
+  else{
+    java.censoringDistribution <- .object_Optional(NULL)
+  }
+  
+  ibsCalculator.java <- .jnew(.class_IBSCalculator, java.censoringDistribution)
+  
+  if(is.null(eventWeights)){
+    eventWeights <- rep(1, times = length(events))
+  }
+  
+  ibsCalculatorWrapper.java <- .jnew(.class_IBSErrorCalculatorWrapper, 
+                                     ibsCalculator.java,
+                                     .jarray(as.integer(events)),
+                                     as.numeric(time),
+                                     .jarray(as.numeric(eventWeights)))
+  
+  ibsCalculatorWrapper.java <- .jcast(ibsCalculatorWrapper.java, .class_ErrorCalculator)
+  return(ibsCalculatorWrapper.java)
+  
+  
+}

R/wihs.R

@@ -15,9 +15,13 @@
 #' @source The data was obtained from the randomForestSRC R package.
 #'
 #' @references Bacon MC, von Wyl V, Alden C, Sharp G, Robison E, Hessol N, Gange
-#'   S, Barranday Y, Holman S, Weber K, Young MA (2005). “The Women’s
-#'   Interagency HIV Study: an Observational Cohort Brings Clinical Sciences to
-#'   the Bench.” Clinical and Vaccine Immunology, 12(9), 1013–1019.
+#' S, Barranday Y, Holman S, Weber K, Young MA (2005). “The Women’s Interagency
+#' HIV Study: an Observational Cohort Brings Clinical Sciences to the Bench.”
+#' Clinical and Vaccine Immunology, 12(9), 1013–1019.
 #' doi:10.1128/CDLI.12.9.1013-1019.2005.
 #'
+#' Ishwaran, H., and Udaya B. Kogalur. 2018. Random Forests for Survival,
+#' Regression, and Classification (Rf-Src). manual.
+#' https://cran.r-project.org/package=randomForestSRC.
+#' 
 "wihs"

java/OBTAINING.md

@@ -2,4 +2,4 @@ The Java source code for this package can be obtained at https://github.com/jath
 
 * Delete the Jar file in `inst/java/`
 * Build the Java code in its own separate directory using `mvn clean package` in the root of the directory (same folder containing `README.md`). Make sure you have [Maven](https://maven.apache.org/) installed.
-* Copy the `library/target/largeRCRF-library/1.0-SNAPSHOT.jar` file produced in the Java directory into `inst/java/`
+* Copy the `library/target/largeRCRF-library-1.0-SNAPSHOT.jar` file produced in the Java directory into `inst/java/`

man/addTrees.Rd

@@ -6,7 +6,8 @@
 \usage{
 addTrees(forest, numTreesToAdd, savePath = NULL,
   savePath.overwrite = c("warn", "delete", "merge"),
-  cores = getCores(), displayProgress = TRUE)
+  forest.output = c("online", "offline"), cores = getCores(),
+  displayProgress = TRUE)
 }
 \arguments{
 \item{forest}{An existing forest.}
@@ -21,6 +22,11 @@ a previously saved forest.}
 directory, possibly containing another forest, this specifies what should
 be done.}
 
+\item{forest.output}{This parameter only applies if \code{savePath} has been
+set; set to 'online' (default) and the saved forest will be loaded into
+memory after being trained. Set to 'offline' and the forest is not saved
+into memory, but can still be used in a memory unintensive manner.}
+
 \item{cores}{The number of cores to be used for training the new trees.}
 
 \item{displayProgress}{A logical indicating whether the progress should be

man/convertToOnlineForest.Rd

@@ -0,0 +1,20 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/convertToOnlineForest.R
+\name{convertToOnlineForest}
+\alias{convertToOnlineForest}
+\title{Convert to Online Forest}
+\usage{
+convertToOnlineForest(forest)
+}
+\arguments{
+\item{forest}{The offline forest.}
+}
+\value{
+An online, in memory forst.
+}
+\description{
+Some forests are too large to store in memory and have been saved to disk.
+They can still be used, but their performance is much slower. If there's
+enough memory, they can easily be converted into an in-memory forest that is
+faster to use.
+}

man/integratedBrierScore.Rd

@@ -0,0 +1,69 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/cr_integratedBrierScore.R
+\name{integratedBrierScore}
+\alias{integratedBrierScore}
+\title{Integrated Brier Score}
+\usage{
+integratedBrierScore(responses, predictions, event, time,
+  censoringDistribution = NULL, parallel = TRUE)
+}
+\arguments{
+\item{responses}{A list of responses corresponding to the provided
+mortalities; use \code{\link{CR_Response}}.}
+
+\item{predictions}{The predictions to be tested against.}
+
+\item{event}{The event type for the error to be calculated on.}
+
+\item{time}{\code{time} specifies the upper bound of the integral.}
+
+\item{censoringDistribution}{Optional; if provided then weights are
+  calculated on the errors. There are three ways to provide it - \itemize{
+  \item{If you have all the censor times and just want to use a simple
+  empirical estimate of the distribution, just provide a numeric vector of
+  all of the censor times and it will be automatically calculated.} \item{You
+  can directly specify the survivor function by providing a list with two
+  numeric vectors called \code{x} and \code{y}. They should be of the same
+  length and correspond to each point. It is assumed that previous to the
+  first value in \code{y} the \code{y} value is 1.0; and that the function
+  you provide is a right-continuous step function.} \item{You can provide a
+  function from \code{\link[stats]{stepfun}}. Note that this only supports
+  functions where \code{right = FALSE} (default), and that the first y value
+  (corresponding to y before the first x value) will be to set to 1.0
+  regardless of what is specified.}
+
+  }}
+
+\item{parallel}{A logical indicating whether multiple cores should be
+utilized when calculating the error. Available as an option because it's
+been observed that using Java's \code{parallelStream} can be unstable on
+some systems. Default value is \code{TRUE}; only set to \code{FALSE} if you
+get strange errors while predicting.}
+}
+\value{
+A numeric vector of the Integrated Brier Score for each prediction.
+}
+\description{
+Used to calculate the Integrated Brier Score, which for the competing risks
+setting is the integral of the squared difference between each observed
+cumulative incidence function (CIF) for each observation and the
+corresponding predicted CIF. If the survivor function (1 - CDF) of the
+censoring distribution is provided, weights can be calculated to account for
+the censoring.
+}
+\examples{
+data <- data.frame(delta=c(1,1,0,0,2,2), T=1:6, x=1:6)
+
+model <- train(CR_Response(delta, T) ~ x, data, ntree=100, numberOfSplits=0, mtry=1, nodeSize=1)
+
+newData <- data.frame(delta=c(1,0,2,1,0,2), T=1:6, x=1:6)
+predictions <- predict(model, newData)
+
+scores <- integratedBrierScore(CR_Response(data$delta, data$T), predictions, 1, 6.0)
+
+}
+\references{
+Section 4.2 of Ishwaran H, Gerds TA, Kogalur UB, Moore RD, Gange
+  SJ, Lau BM (2014). “Random Survival Forests for Competing Risks.”
+  Biostatistics, 15(4), 757–773. doi:10.1093/ biostatistics/kxu010.
+}

man/loadForest.Rd

@@ -4,10 +4,19 @@
 \alias{loadForest}
 \title{Load Random Forest}
 \usage{
-loadForest(directory)
+loadForest(directory, forest.output = c("online", "offline"),
+  maxTreeNum = NULL)
 }
 \arguments{
 \item{directory}{The directory created that saved the previous forest.}
+
+\item{forest.output}{Specifies whether the forest loaded should be loaded
+into memory, or reflect the saved files where only one tree is loaded at a
+time.}
+
+\item{maxTreeNum}{If for some reason you only want to load the number of
+trees up until a certain point, you can specify maxTreeNum as a single
+number.}
 }
 \value{
 A JForest object; see \code{\link{train}} for details.

man/naiveConcordance.Rd

@@ -41,3 +41,9 @@ mortalities <- list(
 naiveConcordance(CR_Response(newData$delta, newData$T), mortalities)
 
 }
+\references{
+Section 3.2 of Wolbers, Marcel, Paul Blanche, Michael T. Koller,
+  Jacqueline C M Witteman, and Thomas A Gerds. 2014. “Concordance for
+  Prognostic Models with Competing Risks.” Biostatistics 15 (3): 526–39.
+  https://doi.org/10.1093/biostatistics/kxt059.
+}

man/train.Rd

@@ -6,22 +6,23 @@
 \usage{
 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)
+  maxNodeDepth = 1e+05, na.penalty = TRUE, splitPureNodes = TRUE,
+  savePath = NULL, savePath.overwrite = c("warn", "delete", "merge"),
+  forest.output = c("online", "offline"), cores = getCores(),
+  randomSeed = NULL, displayProgress = TRUE)
 }
 \arguments{
 \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}}
+constructed.}
 
 \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{CompetingRiskSplitFinders}}
-or \code{\link{WeightedVarianceSplitFinder}}. If you don't specify one,
-this function tries to pick one based on the response. For
+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
@@ -30,20 +31,20 @@ 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{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}}.}
+\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}}.}
 
 \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{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}}.}
+\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}}.}
 
 \item{ntree}{An integer that specifies how many trees should be trained.}
 
@@ -66,6 +67,21 @@ as large as \code{nodeSize}.}
 controls tree length; by default \code{maxNodeDepth} is an extremely high
 number and tree depth is controlled by \code{nodeSize}.}
 
+\item{na.penalty}{This parameter controls whether predictor variables with
+NAs should be penalized when being considered for a best split. Best splits
+(and the associated score) are determined on only non-NA data; the penalty
+is to take the best split identified, and to randomly assign any NAs
+(according to the proportion of data split left and right), and then
+recalculate the corresponding split score, when is then compared with the
+other split candiate variables. This penalty adds some computational time,
+so it may be disabled for some variables. \code{na.penalty} may be
+specified as a vector of logicals indicating, for each predictor variable,
+whether the penalty should be applied to that variable. If it's length 1
+then it applies to all variables. Alternatively, a single numeric value may
+be provided to indicate a threshold whereby the penalty is activated only
+if the proportion of NAs for that variable in the training set exceeds that
+threshold.}
+
 \item{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
@@ -91,6 +107,11 @@ 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.}
 
+\item{forest.output}{This parameter only applies if \code{savePath} has been
+set; set to 'online' (default) and the saved forest will be loaded into
+memory after being trained. Set to 'offline' and the forest is not saved
+into memory, but can still be used in a memory unintensive manner.}
+
 \item{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

man/vimp.Rd

@@ -0,0 +1,48 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/vimp.R
+\name{vimp}
+\alias{vimp}
+\title{Variable Importance}
+\usage{
+vimp(forest, newData = NULL, randomSeed = NULL, type = c("mean", "z",
+  "raw"), events = NULL, time = NULL, censoringDistribution = NULL,
+  eventWeights = NULL)
+}
+\arguments{
+\item{forest}{The forest that was trained.}
+
+\item{newData}{A test set of the data if available. If not, then out of bag
+errors will be attempted on the training set.}
+
+\item{randomSeed}{The source of randomness used to permute the values. Can be
+left blank.}
+
+\item{events}{If using competing risks forest, the events that the error
+measure used for VIMP should be calculated on.}
+
+\item{time}{If using competing risks forest, the upper bound of the
+integrated Brier score.}
+
+\item{censoringDistribution}{(Optional) If using competing risks forest, the
+censoring distribution. See \code{\link{integratedBrierScore} for details.}}
+
+\item{eventWeights}{(Optional) If using competing risks forest, weights to be
+applied to the error for each of the \code{events}.}
+}
+\value{
+A named numeric vector of importance values.
+}
+\description{
+Calculate variable importance by recording the increase in error when a given
+predictor is randomly permuted. Regression forests uses mean squared error;
+competing risks uses integrated Brier score.
+}
+\examples{
+data(wihs)
+
+forest <- train(CR_Response(status, time) ~ ., wihs,
+ ntree = 100, numberOfSplits = 0, mtry=3, nodeSize = 5)
+
+vimp(forest, events = 1:2, time = 8.0)
+
+}

man/wihs.Rd

@@ -25,9 +25,13 @@ time may also be censored.
 }
 \references{
 Bacon MC, von Wyl V, Alden C, Sharp G, Robison E, Hessol N, Gange
-  S, Barranday Y, Holman S, Weber K, Young MA (2005). “The Women’s
-  Interagency HIV Study: an Observational Cohort Brings Clinical Sciences to
-  the Bench.” Clinical and Vaccine Immunology, 12(9), 1013–1019.
-  doi:10.1128/CDLI.12.9.1013-1019.2005.
+S, Barranday Y, Holman S, Weber K, Young MA (2005). “The Women’s Interagency
+HIV Study: an Observational Cohort Brings Clinical Sciences to the Bench.”
+Clinical and Vaccine Immunology, 12(9), 1013–1019.
+doi:10.1128/CDLI.12.9.1013-1019.2005.
+
+Ishwaran, H., and Udaya B. Kogalur. 2018. Random Forests for Survival,
+Regression, and Classification (Rf-Src). manual.
+https://cran.r-project.org/package=randomForestSRC.
 }
 \keyword{datasets}

tests/testthat/test_adding_new_trees.R

@@ -1,6 +1,6 @@
 context("Add trees on existing forest")
 
-test_that("Can add trees on existing forest", {
+test_that("Can add trees on existing online forest", {
 
   trainingData <- data.frame(x=rnorm(100))
   trainingData$T <- rexp(100) + abs(trainingData$x)
@@ -20,6 +20,44 @@ test_that("Can add trees on existing forest", {
   
 })
 
+test_that("Can add trees on existing offline forest", {
+  
+  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)
+  trainingData$delta <- sample(0:2, size = 100, replace=TRUE)
+  
+  forest <- train(CR_Response(delta, T) ~ x, trainingData, ntree=50, 
+                  numberOfSplits=0, mtry=1, nodeSize=5, 
+                  forestResponseCombiner = CR_FunctionCombiner(events = 1:2, times = 0:10), # TODO - remove specifing times; this is workaround around unimplemented feature for offline forests
+                  cores=2, displayProgress=FALSE, savePath="trees",
+                  forest.output = "offline")
+  warning("TODO - need to implement feature; test workaround in the meantime")
+  
+  predictions <- predict(forest)
+  
+  warning_message <- "Assuming that the previous forest at savePath is the provided forest argument; if not true then your results will be suspect"
+  
+  forest.more <- expect_warning(addTrees(forest, 50, cores=2, displayProgress=FALSE,
+                          savePath="trees", savePath.overwrite = "merge",
+                          forest.output = "offline"), fixed=warning_message) # test multi-core
+  
+  predictions <- predict(forest)
+  
+  forest.more <- expect_warning(addTrees(forest, 50, cores=1, displayProgress=FALSE,
+                                         savePath="trees", savePath.overwrite = "merge",
+                                         forest.output = "offline"), fixed=warning_message) # test single-core
+  
+  expect_true(T) # show Ok if we got this far
+  
+  unlink("trees", recursive=TRUE)
+  
+})
+
 test_that("Test adding trees on saved forest - using delete", {
   
   if(file.exists("trees")){ # folder could exist from a previous failed test; delete it

tests/testthat/test_brier_score.R

@@ -0,0 +1,38 @@
+context("Calculate integrated Brier score without error")
+
+# This code is more concerned that the code runs without error. The tests in the
+# Java code check that the results it returns are accurate.
+test_that("Can calculate Integrated Brier Score", {
+
+  sampleData <- data.frame(x=rnorm(100))
+  sampleData$T <- sample(0:4, size=100, replace = TRUE) # the censor distribution we provide needs to conform to the data or we can get NaNs
+  sampleData$delta <- sample(0:2, size = 100, replace = TRUE)
+  
+  testData <- sampleData[1:5,]
+  trainingData <- sampleData[6:100,]
+  
+  forest <- train(CR_Response(delta, T) ~ x, trainingData, ntree=50, numberOfSplits=0, mtry=1, nodeSize=5, cores=2, displayProgress=FALSE)
+  
+  predictions <- predict(forest, testData)
+  
+  scores_test <- integratedBrierScore(CR_Response(testData$delta, testData$T), predictions, event = 1, time = 4,
+                                 censoringDistribution = NULL)
+  # Check that we don't get a crash if we calculate the error for only one observation
+  scores_one <- integratedBrierScore(CR_Response(testData$delta, testData$T)[1], predictions[1], event = 1, time = 4,
+                                 censoringDistribution = NULL)
+  
+  # Make sure our error didn't somehow change
+  expect_equal(scores_one, scores_test[1])
+  
+  # Provide a censoring distribution via censor times
+  scores_censoring1 <- integratedBrierScore(CR_Response(testData$delta, testData$T), predictions, event = 1, time = 4,
+                                      censoringDistribution = c(0,1,1,2,3,4))
+  scores_censoring2 <- integratedBrierScore(CR_Response(testData$delta, testData$T), predictions, event = 1, time = 4,
+                                     censoringDistribution = list(x = 0:4, y = 1 - c(1/6, 3/6, 4/6, 5/6, 6/6)))
+  scores_censoring3 <- integratedBrierScore(CR_Response(testData$delta, testData$T), predictions, event = 1, time = 4,
+                                            censoringDistribution = stepfun(x=0:4, y=1 - c(0, 1/6, 3/6, 4/6, 5/6, 6/6)))
+  
+  expect_equal(scores_censoring1, scores_censoring2)
+  expect_equal(scores_censoring1, scores_censoring3)
+  
+})

tests/testthat/test_responses_length_1.R

@@ -15,3 +15,44 @@ test_that("CR_Response of length 1 - no censor times", {
   expect_true(T) # show Ok if we got this far
   
 })
+
+test_that("Can sub-index CR_Response - no censor times", {
+  
+  x <- CR_Response(1:5, 1:5)
+  
+  index <- 5
+  
+  y <- x[index]
+  
+  expect_equal(y$eventTime, index)
+  expect_equal(y$eventIndicator, index)
+  
+  expect_equal(rJava::.jcall(y$javaObject, "I", "size"), 1)
+  oneJavaItem <- rJava::.jcall(y$javaObject, largeRCRF:::makeResponse(largeRCRF:::.class_Object), "get", 0L)
+  oneJavaItem <- rJava::.jcast(oneJavaItem, largeRCRF:::.class_CompetingRiskResponse)
+  delta <- rJava::.jcall(oneJavaItem, "I", "getDelta")
+  
+  expect_equal(delta, index)
+  
+})
+
+test_that("Can sub-index CR_Response - censor times", {
+  
+  x <- CR_Response(1:5, 1:5, 1:5)
+  
+  index <- 5
+  
+  y <- x[index]
+  
+  expect_equal(y$eventTime, index)
+  expect_equal(y$eventIndicator, index)
+  expect_equal(y$censorTime, index)
+  
+  expect_equal(rJava::.jcall(y$javaObject, "I", "size"), 1)
+  oneJavaItem <- rJava::.jcall(y$javaObject, largeRCRF:::makeResponse(largeRCRF:::.class_Object), "get", 0L)
+  oneJavaItem <- rJava::.jcast(oneJavaItem, largeRCRF:::.class_CompetingRiskResponseWithCensorTime)
+  delta <- rJava::.jcall(oneJavaItem, "D", "getC")
+  
+  expect_equal(delta, index)
+  
+})

tests/testthat/test_running.R

@@ -28,6 +28,7 @@ test_that("Regresssion doesn't crash", {
   forest <- train(y ~ x, trainingData, ntree=50, numberOfSplits=0, mtry=1, nodeSize=5, cores=2, displayProgress=FALSE)
   
   predictions <- predict(forest, testData)
+  other_predictions <- predict(forest) # there was a bug if newData wasn't provided.
   
   expect_true(T) # show Ok if we got this far
   

tests/testthat/test_saving_loading.R

@@ -2,7 +2,11 @@ context("Train, save, and load without error")
 
 test_that("Can save & load regression example", {
 
-  expect_false(file.exists("trees_saving_loading")) # Folder shouldn't exist yet
+  if(file.exists("trees_saving_loading")){
+    unlink("trees_saving_loading", recursive=TRUE)
+  }
+  
+  expect_false(file.exists("trees_saving_loading")) # Folder shouldn't exist at this point
   
   x1 <- rnorm(1000)
   x2 <- rnorm(1000)

tests/testthat/test_saving_offline.R

@@ -13,7 +13,7 @@ test_that("Can save a random forest while training, and use it afterward", {
   data <- data.frame(x1, x2, y)
   forest <- train(y ~ x1 + x2, data,
                   ntree=100, numberOfSplits = 5, mtry = 1, nodeSize = 5,
-                  savePath="trees", displayProgress=FALSE)
+                  savePath="trees", forest.output = "online", displayProgress=FALSE)
   
   expect_true(file.exists("trees")) # Something should have been saved
   
@@ -26,6 +26,39 @@ test_that("Can save a random forest while training, and use it afterward", {
   predictions <- predict(newforest, newData)
   
   
+  unlink("trees", recursive=TRUE)
+  
+})
+
+test_that("Can save a random forest while training, and use it afterward with pure offline forest", {
+  
+  if(file.exists("trees")){ # folder could exist from a previous failed test; delete it
+    unlink("trees", recursive=TRUE)
+  }
+  
+  x1 <- rnorm(1000)
+  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,
+                  savePath="trees", forest.output = "offline", displayProgress=FALSE)
+  
+  expect_true(file.exists("trees")) # Something should have been saved
+  
+  # try making a little prediction to verify it works
+  newData <- data.frame(x1=seq(from=-3, to=3, by=0.5), x2=0)
+  predictions <- predict(forest, newData)
+  
+  # Also make sure we can load the forest too
+  newforest <- loadForest("trees")
+  predictions <- predict(newforest, newData)
+  
+  # Last, make sure we can take the forest online
+  onlineForest <- convertToOnlineForest(forest)
+  predictions <- predict(onlineForest, newData)
+  
   unlink("trees", recursive=TRUE)
   
 })

tests/testthat/test_vimp.R

@@ -0,0 +1,102 @@
+context("Use VIMP without error")
+
+test_that("VIMP doesn't crash; no test dataset", {
+
+  data(wihs)
+  
+  forest <- train(CR_Response(status, time) ~ ., wihs, ntree=50, numberOfSplits=0, mtry=1, nodeSize=5, displayProgress=FALSE)
+  
+  # Run VIMP several times under different scenarios
+  importance <- vimp(forest, type="raw", events=1:2, time=5.0)
+  vimp(forest, type="raw", events=1, time=5.0)
+  vimp(forest, type="raw", events=1:2, time=5.0, eventWeights = c(0.2, 0.8))
+  
+  # Not much of a test, but the Java code tests more for correctness. This just
+  # tests that the R code runs without error.
+  expect_equal(ncol(importance), 4) # 4 predictors
+  
+})
+
+
+test_that("VIMP doesn't crash; test dataset", {
+  
+  data(wihs)
+  
+  trainingData <- wihs[1:1000,]
+  testData <- wihs[1001:nrow(wihs),]
+  
+  forest <- train(CR_Response(status, time) ~ ., trainingData, ntree=50, numberOfSplits=0, mtry=1, nodeSize=5, displayProgress=FALSE, cores=1)
+  
+  # Run VIMP several times under different scenarios
+  importance <- vimp(forest, newData=testData, type="raw", events=1:2, time=5.0)
+  vimp(forest, newData=testData, type="raw", events=1, time=5.0)
+  vimp(forest, newData=testData, type="raw", events=1:2, time=5.0, eventWeights = c(0.2, 0.8))
+  
+  # Not much of a test, but the Java code tests more for correctness. This just
+  # tests that the R code runs without error.
+  expect_equal(ncol(importance), 4) # 4 predictors
+  
+})
+
+
+test_that("VIMP doesn't crash; censoring distribution; all methods equal", {
+  
+  sampleData <- data.frame(x=rnorm(100))
+  sampleData$T <- sample(0:4, size=100, replace = TRUE) # the censor distribution we provide needs to conform to the data or we can get NaNs
+  sampleData$delta <- sample(0:2, size = 100, replace = TRUE)
+  
+  testData <- sampleData[1:5,]
+  trainingData <- sampleData[6:100,]
+  
+  forest <- train(CR_Response(delta, T) ~ x, trainingData, ntree=50, numberOfSplits=0, mtry=1, nodeSize=5, cores=2, displayProgress=FALSE)
+  
+  importance1 <- vimp(forest, type="raw", events=1:2, time=4.0, randomSeed=50, 
+                      censoringDistribution = c(0,1,1,2,3,4))
+  importance2 <- vimp(forest, type="raw", events=1:2, time=4.0, randomSeed=50, 
+                      censoringDistribution = list(x = 0:4, y = 1 - c(1/6, 3/6, 4/6, 5/6, 6/6)))
+  importance3 <- vimp(forest, type="raw", events=1:2, time=4.0, randomSeed=50, 
+                      censoringDistribution = stepfun(x=0:4, y=1 - c(0, 1/6, 3/6, 4/6, 5/6, 6/6)))
+  
+  expect_equal(importance1, importance2)
+  expect_equal(importance1, importance3)
+  
+})
+
+test_that("VIMP doesn't crash; regression dataset", {
+  
+  data <- data.frame(x1=rnorm(1000), x2=rnorm(1000), x3=rnorm(1000))
+  data$y <- data$x1 + 3*data$x2 + 0.05*data$x3 + rnorm(1000)
+  
+  forest <- train(y ~ ., data, ntree=50, numberOfSplits=100, mtry=2, nodeSize=5, displayProgress=FALSE)
+  
+  importance <- vimp(forest, type="mean")
+  
+  expect_true(importance["x2"] > importance["x3"])
+  
+  # Not much of a test, but the Java code tests more for correctness. This just
+  # tests that the R code runs without error.
+  expect_equal(length(importance), 3) # 3 predictors
+  
+})
+
+test_that("VIMP produces mean and z scores correctly", {
+  
+  data <- data.frame(x1=rnorm(1000), x2=rnorm(1000), x3=rnorm(1000))
+  data$y <- data$x1 + 3*data$x2 + 0.05*data$x3 + rnorm(1000)
+  
+  forest <- train(y ~ ., data, ntree=50, numberOfSplits=100, mtry=2, nodeSize=5, displayProgress=FALSE)
+  
+  actual.importance.raw <- vimp(forest, type="raw", randomSeed=5)
+  actual.importance.mean <- vimp(forest, type="mean", randomSeed=5)
+  actual.importance.z <- vimp(forest, type="z", randomSeed=5)
+  
+  expected.importance.mean <- apply(actual.importance.raw, 2, mean)
+  expected.importance.z <- apply(actual.importance.raw, 2, function(x){
+    mn <- mean(x)
+    return( mn / (sd(x) / sqrt(length(x))) )
+  })
+  
+  expect_equal(expected.importance.mean, actual.importance.mean)
+  expect_equal(expected.importance.z, actual.importance.z)
+  
+})