ciu: Contextual Importance and Utility

pkgname <- "ciu"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
base::assign(".ExTimings", "ciu-Ex.timings", pos = 'CheckExEnv')
base::cat("name\tuser\tsystem\telapsed\n", file=base::get(".ExTimings", pos = 'CheckExEnv'))
base::assign(".format_ptime",
function(x) {
  if(!is.na(x[4L])) x[1L] <- x[1L] + x[4L]
  if(!is.na(x[5L])) x[2L] <- x[2L] + x[5L]
  options(OutDec = '.')
  format(x[1L:3L], digits = 7L)
},
pos = 'CheckExEnv')

### * </HEADER>
library('ciu')

base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("ciu.new")
### * ciu.new

flush(stderr()); flush(stdout())

base::assign(".ptime", proc.time(), pos = "CheckExEnv")
### Name: ciu.new
### Title: Create CIU object
### Aliases: ciu.new

### ** Examples

# Explaining the classification of an Iris instance with lda model.
# We use a versicolor (instance 100).
library(MASS)
test.ind <- 100
iris_test <- iris[test.ind, 1:4]
iris_train <- iris[-test.ind, 1:4]
iris_lab <- iris[[5]][-test.ind]
model <- lda(iris_train, iris_lab)

# Create CIU object
ciu <- ciu.new(model, Species~., iris)

# This can be used with explain method for getting CIU values
# of one or several inputs. Here we get CIU for all three outputs
# with input feature "Petal.Length" that happens to be the most important.
ciu$explain(iris_test, 1)

# It is, however, more convenient to use one of the graphical visualisations.
# Here's one using ggplot.
ciu$ggplot.col.ciu(iris_test)

# LDA creates very sharp class limits, which can also be seen in the CIU
# explanation. We can study what the underlying model looks like using
# plot.ciu and plot.ciu.3D methods. Here is a 3D plot for all three classes
# as a function of Petal Length&Width. Iris #100 (shown as the red dot)
# is on the ridge of the "versicolor" class, which is quite narrow for
# Petal Length&Width.
par(mfrow=c(1,3))
ciu$plot.ciu.3D(iris_test,c(3,4),1,main=levels(iris$Species)[1],)
ciu$plot.ciu.3D(iris_test,c(3,4),2,main=levels(iris$Species)[2])
ciu$plot.ciu.3D(iris_test,c(3,4),3,main=levels(iris$Species)[3])
par(mfrow=c(1,1))

# Same thing with a regression task, the Boston Housing data set. Instance
# #370 has the highest valuation (50k$). Model is gbm, which performs
# decently here. Plotting with "standard" bar plot this time.
library(caret)
gbm <- train(medv ~ ., Boston, method="gbm", trControl=trainControl(method="cv", number=10))
ciu <- ciu.new(gbm, medv~., Boston)
ciu$barplot.ciu(Boston[370,1:13])

# Same but sort by CI.
ciu$barplot.ciu(Boston[370,1:13], sort = "CI")




base::assign(".dptime", (proc.time() - get(".ptime", pos = "CheckExEnv")), pos = "CheckExEnv")
base::cat("ciu.new", base::get(".format_ptime", pos = 'CheckExEnv')(get(".dptime", pos = "CheckExEnv")), "\n", file=base::get(".ExTimings", pos = 'CheckExEnv'), append=TRUE, sep="\t")
graphics::par(get("par.postscript", pos = 'CheckExEnv'))
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')