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#Class implementing an Associative Classification Algorithm
#Implements the CBA-C of KEEL
CBA_C <- function(train, test, min_support=0.01, min_confidence=0.5,
pruning=TRUE, maxCandidates=80000){
alg <- RKEEL::R6_CBA_C$new()
alg$setParameters(train, test, min_support, min_confidence, pruning,
maxCandidates)
return (alg)
}
R6_CBA_C <- R6::R6Class("R6_CBA_C",
inherit = AssociativeClassificationAlgorithm,
public = list(
#Public properties
#Minimum Support
min_support = 0.01,
#Minimum Confidence
min_confidence = 0.5,
#Pruning rules?
pruning = TRUE,
#Maximum candidate rules (if 0, not limited)
maxCandidates = 80000,
#Public functions
#Initialize function
setParameters = function(train, test, min_support=0.01, min_confidence=0.5,
pruning=TRUE, maxCandidates=80000){
super$setParameters(train, test)
#Check for constraints
stopText <- ""
if((hasMissingValues(train)) || (hasMissingValues(test))){
stopText <- paste0(stopText, "Dataset has missing values and the algorithm does not accept it.\n")
}
if((hasContinuousData(train)) || (hasContinuousData(test))){
stopText <- paste0(stopText, "Dataset has continuous data and the algorithm does not accept it.\n")
}
if(stopText != ""){
stop(stopText)
}
self$min_support <- min_support
self$min_confidence <- min_confidence
self$pruning <- pruning
self$maxCandidates <- maxCandidates
}
),
private = list(
#Private properties
#jar Filename
jarName = "Clas-CBA.jar",
#algorithm name
algorithmName = "CBA-C",
#String with algorithm name
algorithmString = "Classification Based on Associations (CBA)",
#Private functions
#Get the text with the parameters for the config file
getParametersText = function(){
text <- ""
text <- paste0(text, "Minimum support = ", self$min_support, "\n")
text <- paste0(text, "Minimum confidence = ", self$min_confidence, "\n")
if(self$pruning){
text <- paste0(text, "Wether pruning rules or not? (1:yes, 0:no) = 1", "\n")
}
else{
text <- paste0(text, "Wether pruning rules or not? (1:yes, 0:no) = 0", "\n")
}
text <- paste0(text, "Maximum candidate rules (0: no limited) = ", self$maxCandidates, "\n")
return(text)
}
)
)
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