library("arules")
library("testthat")

debug <- FALSE

context("confint")

data("Adult")
## Mine association rules.
rules <- apriori(Adult,
  parameter = list(
    supp = 0.5,
    conf = 0.9,
    target = "rules"
  ), control = list(verb = FALSE))

measures <- c("count",
  "confidence",
  "lift",
  "oddsRatio",
  "phi",
  "support")

# smooth = .5 is Haldane-Anscombe correction

for (m in measures) {
  if (debug) cat("CI for", m, "\n")
  ci <- cbind(measure = interestMeasure(rules, m, smooth = .5), confint(rules, m))
  if (debug) print(ci)
  expect_false(any(ci[,1] < ci[,2] | ci[,1] > ci[,3] | ci[,2] > ci[,3], na.rm = TRUE), 
    info = m)
}

# exact intervals should be tighter
#confint(rules, "oddsRatio_normal") - confint(rules, "oddsRatio")
#confint(rules, "support_normal") - confint(rules, "support")
#confint(rules, "confidence_normal") - confint(rules, "confidence")
#ci <- confint(rules, "confidence")
#ci <- confint(rules, "support")
#ci[,2] - ci[,1]


# is.significant
s <- is.significant(rules, method = "Fisher", alpha = 0.05, adjust = "none")
s2 <- interestMeasure(rules, "Fisher") <= 0.05

expect_equal(s, s2)