Nothing
## ----eval=FALSE---------------------------------------------------------------
# install.packages("SDEFSR")
## ----eval=FALSE---------------------------------------------------------------
# devtools::install_github('aklxao2/SDEFSR')
## ----eval=FALSE---------------------------------------------------------------
# irisFromKEEL <- read.dataset("iris.dat")
# irisFromARFF <- read.dataset("iris.arff")
# irisFromCSV <- read.dataset("iris.csv")
## ----highlight=TRUE-----------------------------------------------------------
library(SDEFSR)
df <- data.frame(matrix(data = runif(1000), ncol = 10))
#Add class attribute (It must be the last attribute and it must be categorical)
df[,11] <- c("Class 0", "Class 1", "Class 2", "Class 3")
SDEFSR_DatasetObject <- SDEFSR_DatasetFromDataFrame(df, relation = "random")
#Load from iris dataset
irisFromDataFrame <- SDEFSR_DatasetFromDataFrame(iris, "iris")
## ----highlight=TRUE-----------------------------------------------------------
summary(irisFromDataFrame)
## ----highlight=TRUE-----------------------------------------------------------
irisFromDataFrame$nVars
irisFromDataFrame$attributeNames
## ----eval=FALSE---------------------------------------------------------------
# > ruleSet <- MESDIF(paramFile = "param.txt")
## ----eval=TRUE,highlight=TRUE-------------------------------------------------
ruleSet <- MESDIF(paramFile = NULL,
training = irisFromDataFrame,
test = NULL,
output = c("optionsFile.txt", "rulesFile.txt", "testQM.txt"),
seed = 0,
nLabels = 3,
nEval = 300,
popLength = 100,
eliteLength = 2,
crossProb = 0.6,
mutProb = 0.01,
RulesRep = "can",
Obj1 = "CSUP",
Obj2 = "CCNF",
Obj3 = "null",
Obj4 = "null",
targetVariable = "Species",
targetClass = "virginica")
## ----highlight=FALSE----------------------------------------------------------
library(ggplot2)
plot(ruleSet)
## ----eval=FALSE---------------------------------------------------------------
# rulesOrderedBySignificance <- orderRules(ruleSet, by = "Significance")
## ----eval=TRUE----------------------------------------------------------------
#Apply filter by unusualness
filteredRules <- ruleSet[Unusualness > 0.05]
#We check only if the number of rules decrease.
#In this case, this value must be 1.
length(filteredRules)
#Also, you can make the filter as complex as you can
#Filter by Unusualness, TPr and number of variables:
filteredRules <- ruleSet[Unusualness > 0.05 & TPr > 0.9 & nVars == 3]
#In this case, any of the rules match the conditions. Therefore, the
#number of rules must be 0.
length(filteredRules)
## ----eval=FALSE---------------------------------------------------------------
# SDR_GUI()
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