Description Usage Arguments Details Value Note Author(s) References See Also Examples
Measure input importance (including sensitivity analysis) given a supervised data mining model.
1 2 3 4 5 
M 
fitted model, typically is the object returned by 
data 
training data (the same data.frame that was used to fit the model, currently only used to add data histogram to VEC curve). 
RealL 
the number of sensitivity analysis levels (e.g. 7). Note: you need to use 
method 
input importance method. Options are:

measure 
sensitivity analysis measure (used to measure input importance). Options are:

sampling 
for numeric inputs, the sampling scan function. Options are:

baseline 
baseline vector used during the sensitivity analysis. Options are:

responses 
if 
outindex 
the output index (column) of 
task 
the 
PRED 
the prediction function of 
interactions 
numeric vector with the attributes (columns) used by IthD sensitivity analysis (2D or higher, "GSA" method):

Aggregation 
numeric value that sets the number of multimetric aggregation function (used only for "DSA", ""). Options are:

LRandom 
number of samples used by DSA and MSA methods. The default value is 1, which means: use a number equal to training set size. If a different value is used (1<= value <= number of training samples), then LRandom samples are randomly selected. 
MRandom 
sampling type used by MSA: "discrete" (default discrete uniform distribution) or "continuous" (from continuous uniform distribution). 
Lfactor 
sets the maximum number of sensitivity levels for discrete inputs. if FALSE then a maximum of up to RealL levels are used (most frequent ones), else (TRUE) then all levels of the input are used in the SA analysis. 
This function provides several algorithms for measuring input importance of supervised data mining models and the average effect of a given input (or pair of inputs) in the model. A particular emphasis is given on sensitivity analysis (SA), which is a simple method that measures the effects on the output of a given model when the inputs are varied through their range of values. Check the references for more details.
A list
with the components:
$value – numeric vector with the computed sensitivity analysis measure for each attribute.
$imp – numeric vector with the relative importance for each attribute (only makes sense for 1D analysis).
$sresponses – vector list as described in the Value documentation of mining
.
$data – if DSA or MSA, store the used data samples, needed for visualizations made by vecplot.
$method – SA method
$measure – SA measure
$agg – Aggregation value
$nclasses – if task="prob" or "class", the number of output classes, else nclasses=1
$inputs – indexes of the input attributes
$Llevels – sensitivity levels used for each attribute (NA means output attribute)
$interactions – which attributes were interacted when method=GSA.
See also http://www3.dsi.uminho.pt/pcortez/rminer.html
Paulo Cortez http://www3.dsi.uminho.pt/pcortez
To cite the Importance function, sensitivity analysis methods or synthetic datasets, please use:
P. Cortez and M.J. Embrechts.
Using Sensitivity Analysis and Visualization Techniques to Open Black Box Data Mining Models.
In Information Sciences, Elsevier, 225:117, March 2013.
http://dx.doi.org/10.1016/j.ins.2012.10.039
vecplot
, fit
, mining
, mgraph
, mmetric
, savemining
.
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### dontrun is used when the execution of the example requires some computational effort.
### 1st example, regression, 1D sensitivity analysis
## Not run:
data(sa_ssin) # x1 should account for 55
M=fit(y~.,sa_ssin,model="ksvm")
I=Importance(M,sa_ssin,method="1DSA") # 1D SA, AAD
print(round(I$imp,digits=2))
L=list(runs=1,sen=t(I$imp),sresponses=I$sresponses)
mgraph(L,graph="IMP",leg=names(sa_ssin),col="gray",Grid=10)
mgraph(L,graph="VEC",xval=1,Grid=10,data=sa_ssin,
main="VEC curve for x1 influence on y") # or:
vecplot(I,xval=1,Grid=10,data=sa_ssin,datacol="gray",
main="VEC curve for x1 influence on y") # same graph
vecplot(I,xval=c(1,2,3),pch=c(1,2,3),Grid=10,
leg=list(pos="bottomright",leg=c("x1","x2","x3"))) # all x1, x2 and x3 VEC curves
## End(Not run)
### 2nd example, regression, DSA sensitivity analysis:
## Not run:
I2=Importance(M,sa_ssin,method="DSA")
print(I2)
# influence of x1 and x2 over y
vecplot(I2,graph="VEC",xval=1) # VEC curve
vecplot(I2,graph="VECB",xval=1) # VEC curve with boxplots
vecplot(I2,graph="VEC3",xval=c(1,2)) # VEC surface
vecplot(I2,graph="VECC",xval=c(1,2)) # VEC contour
## End(Not run)
### 3th example, classification (pure class labels, task="cla"), DSA:
## Not run:
data(sa_int2_3c) # pair (x1,x2) is more relevant than x3, all x1,x2,x3 affect y,
# x4 has a null effect.
M2=fit(y~.,sa_int2_3c,model="mlpe",task="class")
I4=Importance(M2,sa_int2_3c,method="DSA")
# VEC curve (should present a kind of "saw" shape curve) for class B (TC=2):
vecplot(I4,graph="VEC",xval=2,cex=1.2,TC=2,
main="VEC curve for x2 influence on y (class B)",xlab="x2")
# same VEC curve but with boxplots:
vecplot(I4,graph="VECB",xval=2,cex=1.2,TC=2,
main="VEC curve with box plots for x2 influence on y (class B)",xlab="x2")
## End(Not run)
### 4th example, regression, DSA:
## Not run:
data(sa_psin)
# same model from Table 1 of the reference:
M3=fit(y~.,sa_psin,model="ksvm",search=2^2,C=2^6.87,epsilon=2^8)
# in this case: Aggregation is the same as NY
I5=Importance(M3,sa_psin,method="DSA",Aggregation=3)
# 2D analysis (check reference for more details), RealL=L=7:
# need to aggregate results into a matrix of SA measure
cm=agg_matrix_imp(I5)
print("show Table 8 DSA results (from the reference):")
print(round(cm$m1,digits=2))
print(round(cm$m2,digits=2))
# show most relevant (darker) input pairs, in this case (x1,x2) > (x1,x3) > (x2,x3)
# to build a nice plot, a fixed threshold=c(0.05,0.05) is used. note that
# in the paper and for real data, we use threshold=0.1,
# which means threshold=rep(max(cm$m1,cm$m2)*threshold,2)
fcm=cmatrixplot(cm,threshold=c(0.05,0.05))
# 2D analysis using pair AT=c(x1,x2') (check reference for more details), RealL=7:
# nice 3D VEC surface plot:
vecplot(I5,xval=c(1,2),graph="VEC3",xlab="x1",ylab="x2",zoom=1.1,
main="VEC surface of (x1,x2') influence on y")
# same influence but know shown using VEC contour:
par(mar=c(4.0,4.0,1.0,0.3)) # change the graph window space size
vecplot(I5,xval=c(1,2),graph="VECC",xlab="x1",ylab="x2",
main="VEC surface of (x1,x2') influence on y")
# slower GSA:
I6=Importance(M3,sa_psin,method="GSA",interactions=1:4)
cm2=agg_matrix_imp(I6)
# compare cm2 with cm1, almost identical:
print(round(cm2$m1,digits=2))
print(round(cm2$m2,digits=2))
fcm2=cmatrixplot(cm2,threshold=0.1)
## End(Not run)
### If you want to use Importance over your own model (different than rminer ones):
# 1st example, regression, uses the theoretical sin1reg function: x1=70% and x2=30%
data(sin1reg)
mypred=function(M,data)
{ return (M[1]*sin(pi*data[,1]/M[3])+M[2]*sin(pi*data[,2]/M[3])) }
M=c(0.7,0.3,2000)
# 4 is the column index of y
I=Importance(M,sin1reg,method="sens",measure="AAD",PRED=mypred,outindex=4)
print(I$imp) # x1=72.3% and x2=27.7%
L=list(runs=1,sen=t(I$imp),sresponses=I$sresponses)
mgraph(L,graph="IMP",leg=names(sin1reg),col="gray",Grid=10)
mgraph(L,graph="VEC",xval=1,Grid=10) # equal to:
par(mar=c(2.0,2.0,1.0,0.3)) # change the graph window space size
vecplot(I,graph="VEC",xval=1,Grid=10,main="VEC curve for x1 influence on y:")
### 2nd example, 3class classification for iris and lda model:
## Not run:
data(iris)
library(MASS)
predlda=function(M,data) # the PRED function
{ return (predict(M,data)$posterior) }
LDA=lda(Species ~ .,iris, prior = c(1,1,1)/3)
# 4 is the column index of Species
I=Importance(LDA,iris,method="1DSA",PRED=predlda,outindex=4)
vecplot(I,graph="VEC",xval=1,Grid=10,TC=1,
main="1D VEC for Sepal.Lenght (xaxis) influence in setosa (prob.)")
## End(Not run)
### 3rd example, binary classification for setosa iris and lda model:
## Not run:
iris2=iris;iris2$Species=factor(iris$Species=="setosa")
predlda2=function(M,data) # the PRED function
{ return (predict(M,data)$class) }
LDA2=lda(Species ~ .,iris2)
I=Importance(LDA2,iris2,method="1DSA",PRED=predlda2,outindex=4)
vecplot(I,graph="VEC",xval=1,
main="1D VEC for Sepal.Lenght (xaxis) influence in setosa (class)",Grid=10)
## End(Not run)

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