ciu (version 0.8)

ciu.blackbox.new: Create CIU.BlackBox object

Description

This method mainly serves as an "interface specification" for objects of class CIU.BlackBox, i.e. it defines what method(s) have to be implemented by any object of class CIU.BlackBox. A CIU.BlackBox object is actually a list.

Usage

ciu.blackbox.new()

Value

Object of class CIU.BlackBox.

Author

Kary Främling

Details

An alternative and simpler (but less flexible) way to do the same is to use the predict.function parameter of ciu.new, where predict.function <- function(model, inputs) {predict(model,inputs,n.trees=10000)} would accomplish the same as for the Example below. An example using this approach is also included in Examples.

The advantage of using a CIU.BlackBox wrapper (rather than the simplee predict.function approach) is that it is possible to keep object variables or maintain whatever state information might be needed between calls.

The only things that are actually required from a CIU.BlackBox object is:

1. That it is a list with an element called eval.

2. That the value of eval element is a function of the form eval = function(inputs)

3. That it inherits the class CIU.BlackBox.

Examples

# Create CIU.BlackBox wrapper for Gradient Boosting
library(MASS) # Just in case Boston is not already available
library(gbm)
gbm.ciu.bb <- function(gbm, n.trees=1) {
  o.gbm <- gbm
  o.n.trees <- n.trees
  pub <- list(eval = function(inputs) { predict(o.gbm,inputs,n.trees=o.n.trees) })
  class(pub) <- c("CIU.BlackBox",class(pub))
  return(pub)
}
# \donttest{
# Train and explain. We don't care about training/test sets here.
gbm.Boston <- gbm(medv ~ . ,data = Boston, distribution = "gaussian",
n.trees=10000, shrinkage = 0.01, interaction.depth = 4)
gbm.ciu <- gbm.ciu.bb(gbm.Boston, 10000)
ciu <- ciu.new(gbm.ciu, medv~., Boston)
ciu$barplot.ciu(Boston[370,1:13], sort = "CI")

# Same but using `predict.function` parameter in `ciu.new`.
# Using `ggplot.col.ciu` here for a change.
predict.function <- function(model, inputs) {predict(model,inputs,n.trees=10000)}
ciu <- ciu.new(gbm.Boston, medv~., Boston, predict.function=predict.function)
ciu$ggplot.col.ciu(Boston[370,1:13], sort = "CI")
# }