R/forestFloor_source.R

In sorhawell/happytest: Visualizes Random Forests with Feature Contributions

forestFloor = function(rf.fit,
                       X,
                       Xtest = NULL,
                       calc_np = FALSE,
                       binary_reg = FALSE,
                       bootstrapFC = FALSE,
                       ...) {
  Class = class(rf.fit)[1] #read only first class
  
  #convert randomForest.formula
  if(inherits(rf.fit,"randomForest.formula")){
    X <- as.data.frame(X)
    rn <- row.names(X)
    Terms <- delete.response(rf.fit$terms)
    X <- model.frame(Terms, X, na.action = na.fail)
  }
    
  #randomForest::randomForest or trimTrees::cinbag or rfPermute::rfPermute
  if(inherits(rf.fit,"randomForest")) {
    if(Class=="rfPermute") print("class 'rfPermute' supported as 'randomForest'")
    Type = rf.fit$type
    #changed classification to binary regression if requested and only two classes
    if(binary_reg) {
      if(!is.null(rf.fit$forest$nclass) && rf.fit$forest$nclass==2) {
        Type="regression"
      } else {
        warning("binary_reg=T is not possible for >2 classes. 
                Continue computation as multiClass")
        
      }
    }
  
    #dispatch either forestFloor_regression(and binary) or multiClassification
    switch(Type,
           regression = return(
             forestFloor_randomForest_regression(
               rf.fit      = rf.fit,
               X           = X,
               Xtest       = Xtest,
               calc_np     = calc_np,
               binary_reg  = binary_reg,
               bootstrapFC = bootstrapFC,
               ...)
           ),
           classification = return(
             forestFloor_randomForest_multiClass(
               rf.fit      = rf.fit,
               X           = X,
               Xtest       = Xtest,
               calc_np     = calc_np,
               binary_reg  = binary_reg,
               bootstrapFC = bootstrapFC,
               ...)
           ),#majorityTerminal
           stop("type of randomForest object is neither 'regression' or 'classification', (RF.fit$type==?)"))
  }
  
  #other classes not supported...
  if(Class=="forestFloor_external") {
    print("forestFloor_external is a standardised treemodelfit which is not implemented yet")
    return("... cold emptyness")
  }
  
  #if not classses recognized
  stop("This class is not yet supported, is this a random forest model fit?")

}