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Adding the possibility to skip pruning step when using CBA function #31

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Adding the possibility to skip pruning step when using CBA function #31

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8 changes: 5 additions & 3 deletions R/CBA.R
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
#' of form \code{class ~ .} or \code{class ~ predictor1 + predictor2}.
#' @param data A data.frame or a transaction set containing the training data.
#' Data frames are automatically discretized and converted to transactions.
#' @param pruning Pruning strategy used: "M1" or "M2".
#' @param pruning Pruning strategy used: "M1" or "M2". NULL to skip pruning step.
#' @param parameter,control Optional parameter and control lists for apriori.
#' @param balanceSupport balanceSupport parameter passed to
#' \code{\link{mineCARs}} function.
Expand Down Expand Up @@ -79,8 +79,10 @@ CBA <- function(formula, data, pruning = "M1",
verbose = verbose, ...)

if(verbose) cat("\nPruning CARs...\n")
if(pruning == "M1") rulebase <- pruneCBA_M1(formula, rulebase, trans)
else rulebase <- pruneCBA_M2(formula, rulebase, trans)
if(!is.null(pruning)) {
if(pruning == "M1") rulebase <- pruneCBA_M1(formula, rulebase, trans)
else rulebase <- pruneCBA_M2(formula, rulebase, trans)
}

if(verbose) cat("CARs left:", length(rulebase), "\n")

Expand Down
2 changes: 1 addition & 1 deletion man/CBA.Rd
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25 changes: 20 additions & 5 deletions tests/testthat/test-CBA.R
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Original file line number Diff line number Diff line change
@@ -1,15 +1,30 @@
library("testthat")
library("arulesCBA")
data("iris")

context("CBA")

cba_classifier <- CBA(Species ~ ., iris, supp = 0.05, conf = 0.9, pruning = "M1")
data("iris")
formula <- Species ~ .

trans <- prepareTransactions(formula, iris, disc.method = "mdlp")
rulebase <- mineCARs(
formula = Species ~ .,
transactions = trans,
supp = 0.05,
conf = 0.9
)
n_rules <- length(rulebase)

cba_no_pruning <- CBA(formula, iris, supp = 0.05, conf = 0.9, pruning = NULL)
expect_equal(length(rules(cba_no_pruning)), n_rules)

cba_classifier <- CBA(formula, iris, supp = 0.05, conf = 0.9, pruning = "M1")
expect_equal(length(rules(cba_classifier)), 8L)

results <- predict(cba_classifier, iris)
expect_equal(results[1], factor("setosa",
levels = c("setosa", "versicolor", "virginica")))
expect_equal(
results[1], factor("setosa", levels = c("setosa", "versicolor", "virginica"))
)

results <- predict(cba_classifier, head(iris, n = 5))
expect_equal(length(results), 5L)
Expand All @@ -25,7 +40,7 @@ results <- predict(cba_classifier, head(iris, n = 5))
expect_equal(length(results), 5L)

# FIXME: We need to check what the output of M2 should be
cba_classifier_M2 <- CBA(Species ~ ., iris, supp = 0.05, conf = 0.9, pruning = "M2")
cba_classifier_M2 <- CBA(formula, iris, supp = 0.05, conf = 0.9, pruning = "M2")
# FIXME: there is a bug in totalError calculation in M2
#expect_equal(length(rules(cba_classifier_M2)), 8L)