Nothing
R/rf_viz.R
In rfviz: Interactive Visualization Tool for Random Forests
Defines functions
rf_viz
Documented in
rf_viz
#' @title Random Forest Plots for interpreting Random Forests output
#'
#' @description The Input Data, Local Importance Scores, and Classic Multidimensional Scaling Plots
#'
#' @importFrom stats cmdscale
#' @importFrom utils data
#' @import tcltk
#' @import randomForest
#' @import loon
#'
#' @param rfprep A list of prepared Random Forests input data to be used in visualization, created using the function rf_prep.
#' @param input Should the Input Data Parallel Coordinate Plot be included in the visualization?
#' @param imp Should the Local Importance Scores Parallel Coordinate Plot be included in the visualization?
#' @param cmd Should the Classic Multidimensional Scaling Proximites 3-D XYZ Scatter Plot be included in the visualization?
#' @param hl_color The highlight color when you select points on the plot(s).
#' @return Any combination of the parallel coordinate plots of the input data, the local importance scores, and the 3-D XYZ classic multidimensional scaling proximities from the output of the random forest algorithm.
#'
#' @note For instructions on how to use randomForests, use ?randomForest. For more information on loon, use ?loon.
#'
#' For detailed instructions in the use of these plots in this package, visit \url{https://github.com/chriskuchar/rfviz/blob/master/Rfviz.md}
#'
#' @author Chris Kuchar \email{chrisjkuchar@gmail.com}, based on original Java graphics by Leo
#' Breiman and Adele Cutler.
#'
#' @references
#' Liaw A, Wiener M (2002). “Classification and Regression by randomForest.” _R News_,
#' *2*(3), 18-22. \url{https://CRAN.R-project.org/doc/Rnews/}
#'
#' Waddell A, Oldford R. Wayne (2018). "loon: Interactive Statistical Data Visualization"
#' \url{https://github.com/waddella/loon}
#'
#' Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.
#'
#' Breiman, L (2002), “Manual On Setting Up, Using, And Understanding Random Forests V3.1”,
#' \url{https://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf}
#'
#' Breiman, L., Cutler, A., Random Forests Graphics.
#' \url{https://www.stat.berkeley.edu/~breiman/RandomForests/cc_graphics.htm}
#'
#' @seealso \code{\link[randomForest]{randomForest}}, \code{\link{rf_prep}}, \code{\link[loon]{l_plot3D}}, \code{\link[loon]{l_serialaxes}}
#'
#' @examples
#' #Classification with iris data set
#' rfprep <- rf_prep(x = iris[,1:4], y = iris$Species)
#'
#' #View all three plots
#' Myrfplots <- rf_viz(rfprep, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = 'orange')
#'
#' #Select data on any of the plots then run:
#' iris[Myrfplots$input['selected'], ]
#' iris[Myrfplots$imp['selected'], ]
#' iris[Myrfplots$cmd['selected'], ]
#'
#' #Rotate 3-D XYZ Scatterplot
#' #1. Click on 3-D XYZ Scatterplot
#' #2. Press 'r' on keyboard to enter rotation mode
#' #3. Click and drag mouse to rotate plot
#' #4. Press 'r' to leave rotation mode
#'
#' #View only the Input Data and CMD Scaling Proximities Plots
#' Myrfplots <- rf_viz(rfprep, input = TRUE, imp = FALSE, cmd = TRUE, hl_color = 'orange')
#'
#' #Regression with mtcars data set
#' rfprep2 <- rf_prep(x = mtcars[,-1], y = mtcars$mpg)
#'
#' #View all three plots
#' Myrfplots <- rf_viz(rfprep2, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = 'orange')
#'
#' #Unsupervised clustering with iris data set
#' rfprep <- rf_prep(x = iris[,1:4], y = NULL)
#'
#' #View the Input Data and CMD Scaling Proximities Plots for the unsupervised case.
#' #(Importance Scores Plot not valid here)
#' Myrfplots <- rf_viz(rfprep, input = TRUE, imp = FALSE, cmd = TRUE, hl_color = 'orange')
#' @export
rf_viz <-
function(rfprep,
input = TRUE,
imp = TRUE,
cmd = TRUE,
hl_color = "orange") {
tcl('set', '::loon::Options(select-color)', hl_color)
#This is to pass the CRAN checks
if(is.null(rfprep$y)){
col <- NULL
} else {
col <- rfprep$y
}
if(is.null(rfprep$y)){
imp <- FALSE
}
issa <- NULL
rm(issa)
idsa <- NULL
rm(idsa)
cmdxyz <- NULL
rm(cmdxyz)
cmdxyz <- NULL
rm(cmdxyz)
inputOnly <- FALSE
impOnly <- FALSE
cmdOnly <- FALSE
if(input == TRUE & imp == FALSE & cmd == FALSE) {
inputOnly <- TRUE
} else {
if(input == FALSE & imp == TRUE & cmd == FALSE) {
impOnly <- TRUE
} else {
if(input == FALSE & imp == FALSE & cmd == TRUE) {
cmdOnly <- TRUE
}
}
}
tt <- tktoplevel()
customInspector <- FALSE
if(inputOnly == TRUE | impOnly == TRUE | cmdOnly == TRUE) customInspector <- TRUE
#All three plots, the input data, local importance scores, and cmd scaled proximities
if (input == TRUE) {
#Input Serial Axes Plot
idsa <- l_serialaxes(
parent = tt,
rfprep$x,
linkingGroup = nrow(rfprep$x),
axesLayout = "parallel",
scaling = "variable",
title = "Input Data",
color = col,
useLoonInspector = !customInspector
)
}
if (imp == TRUE) {
#Local Importance Score Plots
#Prepare the Local Importance Scores for the plot
xx <- as.data.frame(rfprep$rf$localImportance)
yy <- t(xx)
zz <- as.data.frame(yy)
issa <-
l_serialaxes(
parent = tt,
data = zz,
linkingGroup = nrow(rfprep$x),
axesLayout = "parallel",
scaling = "variable",
title = "Local Importance Scores",
useLoonInspector = !customInspector,
color = col
)
}
if (cmd == TRUE) {
#Metric Multidimensional Scaling Proximities
#Obtain the CMD scaled proximities in preparation for plot
rf.mds <- cmdscale(1 - rfprep$rf$proximity, eig = TRUE, k = 3)
cmdxyz <-
l_plot3D(rf.mds$points,
parent = tt,
linkingGroup = nrow(rfprep$x),
title = "Metric Multidimensional Scaling Proximities",
useLoonInspector = !customInspector,
color = col,
xlabel = "x",
ylabel = "y",
zlabel = "z",
showLabels=TRUE
)
}
#Setting up the viewing grid for the plots
#All plots
if (input == TRUE & imp == TRUE & cmd == TRUE) {
tkgrid(idsa,
row = 0,
column = 0,
sticky = "nesw")
tkgrid(issa,
row = 0,
column = 1,
sticky = "nesw")
tkgrid(cmdxyz,
row = 1,
column = 0,
sticky = "nesw")
}
if (input == TRUE && imp == TRUE && cmd == FALSE) {
tkgrid(idsa,
row = 0,
column = 0,
sticky = "nesw")
tkgrid(issa,
row = 1,
column = 0,
sticky = "nesw")
}
#Input data and CMD scaling proximities plots
if (input == TRUE & imp == FALSE & cmd == TRUE) {
tkgrid(idsa,
row = 0,
column = 0,
sticky = "nesw")
tkgrid(cmdxyz,
row = 1,
column = 0,
sticky = "nesw")
}
#Local Importance Score and CMD Proximities Plots
if (input == FALSE & imp == TRUE & cmd == TRUE) {
tkgrid(issa,
row = 0,
column = 0,
sticky = "nesw")
tkgrid(cmdxyz,
row = 1,
column = 0,
sticky = "nesw")
}
tkgrid.columnconfigure(tt, 0, weight = 1)
tkgrid.columnconfigure(tt, 1, weight = 1)
tkgrid.rowconfigure(tt, 0, weight = 1)
#Input Data Plot
if (inputOnly == TRUE) {
tkpack(idsa,
side = "left",
fill = "both",
expand = TRUE)
## Add a custom inspector (no layers)
f <- tkframe(tt)
ai <- l_serialaxes_inspector(parent = f, activewidget = idsa)
tkpack(f, side = "right", anchor = "ne")
tkpack(ai, side = "top", fill = "x")
}
#Local Importance Score Plot
if (impOnly == TRUE) {
tkpack(issa,
side = "left",
fill = "both",
expand = TRUE)
## Add a custom inspector (no layers)
f <- tkframe(tt)
ai <- l_serialaxes_inspector(parent = f, activewidget = issa)
tkpack(f, side = "right", anchor = "ne")
tkpack(ai, side = "top", fill = "x")
}
#CMD Scaling Proximities Plot
if (cmdOnly == TRUE) {
tkpack(
cmdxyz,
side = "left",
fill = "both",
expand = TRUE
)
## Add a custom inspector (no layers)
f <- tkframe(tt)
lf <- tkwidget(f, "labelframe", text = "Worldview")
wv <- l_worldview(parent = lf, activewidget = cmdxyz)
tkconfigure(paste(wv, ".canvas", sep = ""),
width = 50,
height = 160)
ai <-
l_plot_inspector_analysis(parent = f, activewidget = cmdxyz)
tkpack(f, side = "right", anchor = "ne")
tkpack(lf, side = "top", fill = "x")
tkpack(wv, side = "top", fill = "x")
tkpack(ai, side = "top", fill = "x")
}
if(cmd & input & imp){
list(input = idsa, imp = issa, cmd = cmdxyz)
} else if(input & imp){
list(input = idsa, imp = issa)
} else if(imp & cmd){
list(imp = issa, cmd = cmdxyz)
} else if(input & cmd){
list(input = idsa, cmd = cmdxyz)
} else if(input){
list(input = idsa)
} else if(imp){
list(imp = issa)
} else {
list(cmd = cmdxyz)
}
}
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rfviz documentation built on March 18, 2022, 5:52 p.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions rf_viz
Documented in rf_viz
#' @title Random Forest Plots for interpreting Random Forests output
#'
#' @description The Input Data, Local Importance Scores, and Classic Multidimensional Scaling Plots
#'
#' @importFrom stats cmdscale
#' @importFrom utils data
#' @import tcltk
#' @import randomForest
#' @import loon
#'
#' @param rfprep A list of prepared Random Forests input data to be used in visualization, created using the function rf_prep.
#' @param input Should the Input Data Parallel Coordinate Plot be included in the visualization?
#' @param imp Should the Local Importance Scores Parallel Coordinate Plot be included in the visualization?
#' @param cmd Should the Classic Multidimensional Scaling Proximites 3-D XYZ Scatter Plot be included in the visualization?
#' @param hl_color The highlight color when you select points on the plot(s).
#' @return Any combination of the parallel coordinate plots of the input data, the local importance scores, and the 3-D XYZ classic multidimensional scaling proximities from the output of the random forest algorithm.
#'
#' @note For instructions on how to use randomForests, use ?randomForest. For more information on loon, use ?loon.
#'
#' For detailed instructions in the use of these plots in this package, visit \url{https://github.com/chriskuchar/rfviz/blob/master/Rfviz.md}
#'
#' @author Chris Kuchar \email{chrisjkuchar@gmail.com}, based on original Java graphics by Leo
#' Breiman and Adele Cutler.
#'
#' @references
#' Liaw A, Wiener M (2002). “Classification and Regression by randomForest.” _R News_,
#' *2*(3), 18-22. \url{https://CRAN.R-project.org/doc/Rnews/}
#'
#' Waddell A, Oldford R. Wayne (2018). "loon: Interactive Statistical Data Visualization"
#' \url{https://github.com/waddella/loon}
#'
#' Breiman, L. (2001), Random Forests, Machine Learning 45(1), 5-32.
#'
#' Breiman, L (2002), “Manual On Setting Up, Using, And Understanding Random Forests V3.1”,
#' \url{https://www.stat.berkeley.edu/~breiman/Using_random_forests_V3.1.pdf}
#'
#' Breiman, L., Cutler, A., Random Forests Graphics.
#' \url{https://www.stat.berkeley.edu/~breiman/RandomForests/cc_graphics.htm}
#'
#' @seealso \code{\link[randomForest]{randomForest}}, \code{\link{rf_prep}}, \code{\link[loon]{l_plot3D}}, \code{\link[loon]{l_serialaxes}}
#'
#' @examples
#' #Classification with iris data set
#' rfprep <- rf_prep(x = iris[,1:4], y = iris$Species)
#'
#' #View all three plots
#' Myrfplots <- rf_viz(rfprep, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = 'orange')
#'
#' #Select data on any of the plots then run:
#' iris[Myrfplots$input['selected'], ]
#' iris[Myrfplots$imp['selected'], ]
#' iris[Myrfplots$cmd['selected'], ]
#'
#' #Rotate 3-D XYZ Scatterplot
#' #1. Click on 3-D XYZ Scatterplot
#' #2. Press 'r' on keyboard to enter rotation mode
#' #3. Click and drag mouse to rotate plot
#' #4. Press 'r' to leave rotation mode
#'
#' #View only the Input Data and CMD Scaling Proximities Plots
#' Myrfplots <- rf_viz(rfprep, input = TRUE, imp = FALSE, cmd = TRUE, hl_color = 'orange')
#'
#' #Regression with mtcars data set
#' rfprep2 <- rf_prep(x = mtcars[,-1], y = mtcars$mpg)
#'
#' #View all three plots
#' Myrfplots <- rf_viz(rfprep2, input = TRUE, imp = TRUE, cmd = TRUE, hl_color = 'orange')
#'
#' #Unsupervised clustering with iris data set
#' rfprep <- rf_prep(x = iris[,1:4], y = NULL)
#'
#' #View the Input Data and CMD Scaling Proximities Plots for the unsupervised case.
#' #(Importance Scores Plot not valid here)
#' Myrfplots <- rf_viz(rfprep, input = TRUE, imp = FALSE, cmd = TRUE, hl_color = 'orange')
#' @export
rf_viz <-
function(rfprep,
input = TRUE,
imp = TRUE,
cmd = TRUE,
hl_color = "orange") {
tcl('set', '::loon::Options(select-color)', hl_color)
#This is to pass the CRAN checks
if(is.null(rfprep$y)){
col <- NULL
} else {
col <- rfprep$y
}
if(is.null(rfprep$y)){
imp <- FALSE
}
issa <- NULL
rm(issa)
idsa <- NULL
rm(idsa)
cmdxyz <- NULL
rm(cmdxyz)
cmdxyz <- NULL
rm(cmdxyz)
inputOnly <- FALSE
impOnly <- FALSE
cmdOnly <- FALSE
if(input == TRUE & imp == FALSE & cmd == FALSE) {
inputOnly <- TRUE
} else {
if(input == FALSE & imp == TRUE & cmd == FALSE) {
impOnly <- TRUE
} else {
if(input == FALSE & imp == FALSE & cmd == TRUE) {
cmdOnly <- TRUE
}
}
}
tt <- tktoplevel()
customInspector <- FALSE
if(inputOnly == TRUE | impOnly == TRUE | cmdOnly == TRUE) customInspector <- TRUE
#All three plots, the input data, local importance scores, and cmd scaled proximities
if (input == TRUE) {
#Input Serial Axes Plot
idsa <- l_serialaxes(
parent = tt,
rfprep$x,
linkingGroup = nrow(rfprep$x),
axesLayout = "parallel",
scaling = "variable",
title = "Input Data",
color = col,
useLoonInspector = !customInspector
)
}
if (imp == TRUE) {
#Local Importance Score Plots
#Prepare the Local Importance Scores for the plot
xx <- as.data.frame(rfprep$rf$localImportance)
yy <- t(xx)
zz <- as.data.frame(yy)
issa <-
l_serialaxes(
parent = tt,
data = zz,
linkingGroup = nrow(rfprep$x),
axesLayout = "parallel",
scaling = "variable",
title = "Local Importance Scores",
useLoonInspector = !customInspector,
color = col
)
}
if (cmd == TRUE) {
#Metric Multidimensional Scaling Proximities
#Obtain the CMD scaled proximities in preparation for plot
rf.mds <- cmdscale(1 - rfprep$rf$proximity, eig = TRUE, k = 3)
cmdxyz <-
l_plot3D(rf.mds$points,
parent = tt,
linkingGroup = nrow(rfprep$x),
title = "Metric Multidimensional Scaling Proximities",
useLoonInspector = !customInspector,
color = col,
xlabel = "x",
ylabel = "y",
zlabel = "z",
showLabels=TRUE
)
}
#Setting up the viewing grid for the plots
#All plots
if (input == TRUE & imp == TRUE & cmd == TRUE) {
tkgrid(idsa,
row = 0,
column = 0,
sticky = "nesw")
tkgrid(issa,
row = 0,
column = 1,
sticky = "nesw")
tkgrid(cmdxyz,
row = 1,
column = 0,
sticky = "nesw")
}
if (input == TRUE && imp == TRUE && cmd == FALSE) {
tkgrid(idsa,
row = 0,
column = 0,
sticky = "nesw")
tkgrid(issa,
row = 1,
column = 0,
sticky = "nesw")
}
#Input data and CMD scaling proximities plots
if (input == TRUE & imp == FALSE & cmd == TRUE) {
tkgrid(idsa,
row = 0,
column = 0,
sticky = "nesw")
tkgrid(cmdxyz,
row = 1,
column = 0,
sticky = "nesw")
}
#Local Importance Score and CMD Proximities Plots
if (input == FALSE & imp == TRUE & cmd == TRUE) {
tkgrid(issa,
row = 0,
column = 0,
sticky = "nesw")
tkgrid(cmdxyz,
row = 1,
column = 0,
sticky = "nesw")
}
tkgrid.columnconfigure(tt, 0, weight = 1)
tkgrid.columnconfigure(tt, 1, weight = 1)
tkgrid.rowconfigure(tt, 0, weight = 1)
#Input Data Plot
if (inputOnly == TRUE) {
tkpack(idsa,
side = "left",
fill = "both",
expand = TRUE)
## Add a custom inspector (no layers)
f <- tkframe(tt)
ai <- l_serialaxes_inspector(parent = f, activewidget = idsa)
tkpack(f, side = "right", anchor = "ne")
tkpack(ai, side = "top", fill = "x")
}
#Local Importance Score Plot
if (impOnly == TRUE) {
tkpack(issa,
side = "left",
fill = "both",
expand = TRUE)
## Add a custom inspector (no layers)
f <- tkframe(tt)
ai <- l_serialaxes_inspector(parent = f, activewidget = issa)
tkpack(f, side = "right", anchor = "ne")
tkpack(ai, side = "top", fill = "x")
}
#CMD Scaling Proximities Plot
if (cmdOnly == TRUE) {
tkpack(
cmdxyz,
side = "left",
fill = "both",
expand = TRUE
)
## Add a custom inspector (no layers)
f <- tkframe(tt)
lf <- tkwidget(f, "labelframe", text = "Worldview")
wv <- l_worldview(parent = lf, activewidget = cmdxyz)
tkconfigure(paste(wv, ".canvas", sep = ""),
width = 50,
height = 160)
ai <-
l_plot_inspector_analysis(parent = f, activewidget = cmdxyz)
tkpack(f, side = "right", anchor = "ne")
tkpack(lf, side = "top", fill = "x")
tkpack(wv, side = "top", fill = "x")
tkpack(ai, side = "top", fill = "x")
}
if(cmd & input & imp){
list(input = idsa, imp = issa, cmd = cmdxyz)
} else if(input & imp){
list(input = idsa, imp = issa)
} else if(imp & cmd){
list(imp = issa, cmd = cmdxyz)
} else if(input & cmd){
list(input = idsa, cmd = cmdxyz)
} else if(input){
list(input = idsa)
} else if(imp){
list(imp = issa)
} else {
list(cmd = cmdxyz)
}
}
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R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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