Nothing
R/interestMeasures.R
In arules: Mining Association Rules and Frequent Itemsets
Defines functions
.stdLift
.RLD
.basicRuleMeasure
.rhsSupport
.getCounts
.conf_boost
.improvement
.hyperConfidence
.hyperLift
.basicItemsetMeasures
.getN
create_measures_doc
#######################################################################
# arules - Mining Association Rules and Frequent Itemsets
# Copyright (C) 2011-2015 Michael Hahsler, Christian Buchta,
# Bettina Gruen and Kurt Hornik
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#' Calculate Additional Interest Measures
#'
#' Provides the generic function `interestMeasure()` and the
#' methods to calculate various additional interest measures for existing sets
#' of [itemsets] or [rules].
#'
#' A searchable list of definitions, equations and
#' references for all available interest measures can be found at
#' \url{https://mhahsler.github.io/arules/docs/measures}. The descriptions
#' are also linked in the list below.
#'
#' The following measures are implemented for **itemsets**:
#' `r arules:::create_measures_doc(arules:::measuresItemsets)`
#'
#' The following measures are implemented for **rules**:
#' `r arules:::create_measures_doc(arules:::measuresRules)`
#'
#' @aliases interestMeasure
#' @family interest measures
#'
#' @param x a set of [itemsets] or [rules].
#' @param measure name or vector of names of the desired interest measures (see
#' the Details section for available measures). If measure is missing then all available
#' measures are calculated.
#' @param transactions the [transactions] used to mine the associations
#' or a set of different transactions to calculate interest measures from
#' (Note: you need to set `reuse = FALSE` in the later case).
#' @param reuse logical indicating if information in the quality slot should be
#' reuse for calculating the measures. This speeds up the process significantly
#' since only very little (or no) transaction counting is necessary if support,
#' confidence and lift are already available. Use `reuse = FALSE` to force
#' counting (might be very slow but is necessary if you use a different set of
#' transactions than was used for mining).
#' @param ... further arguments for the measure calculation. Many measures
#' are based on contingency table counts and zero counts can produce `NaN` values
#' (division by zero). This issue can be resolved by using the additional
#' parameter `smoothCounts` which performs additive smoothing by adds a
#' "pseudo count" of `smoothCounts` to each cell in the contingency
#' table. Use `smoothCounts = 1` or larger values for Laplace smoothing.
#' Use `smoothCounts = .5` for Haldane-Anscombe correction (Haldan, 1940; Anscombe, 1956)
#' which is often used for chi-squared, phi correlation and related measures.
#'
#' @return If only one measure is used, the function returns a numeric vector
#' containing the values of the interest measure for each association in the
#' set of associations `x`.
#'
#' If more than one measures are specified, the result is a data.frame
#' containing the different measures for each association as columns.
#'
#' `NA` is returned for rules/itemsets for which a certain measure is not
#' defined.
#' @author Michael Hahsler
#' @seealso [itemsets], [rules]
#' @references
#' Hahsler, Michael (2015). A Probabilistic Comparison of Commonly Used
#' Interest Measures for Association Rules, 2015, URL:
#' \url{https://mhahsler.github.io/arules/docs/measures}.
#'
#' Haldane, J.B.S. (1940). "The mean and variance of the moments of chi-squared
#' when used as a test of homogeneity, when expectations are small".
#' _Biometrika,_ 29, 133-134.
#'
#' Anscombe, F.J. (1956). "On estimating binomial response relations".
#' _Biometrika,_ 43, 461-464.
#' @keywords models
#' @examples
#'
#' data("Income")
#' rules <- apriori(Income)
#'
#' ## calculate a single measure and add it to the quality slot
#' quality(rules) <- cbind(quality(rules),
#' hyperConfidence = interestMeasure(rules, measure = "hyperConfidence",
#' transactions = Income))
#'
#' inspect(head(rules, by = "hyperConfidence"))
#'
#' ## calculate several measures
#' m <- interestMeasure(rules, c("confidence", "oddsRatio", "leverage"),
#' transactions = Income)
#' inspect(head(rules))
#' head(m)
#'
#' ## calculate all available measures for the first 5 rules and show them as a
#' ## table with the measures as rows
#' t(interestMeasure(head(rules, 5), transactions = Income))
#'
#' ## calculate measures on a different set of transactions (I use a sample here)
#' ## Note: reuse = TRUE (default) would just return the stored support on the
#' ## data set used for mining
#' newTrans <- sample(Income, 100)
#' m2 <- interestMeasure(rules, "support", transactions = newTrans, reuse = FALSE)
#' head(m2)
#'
#' ## calculate all available measures for the 5 frequent itemsets with highest support
#' its <- apriori(Income, parameter = list(target = "frequent itemsets"))
#' its <- head(its, 5, by = "support")
#' inspect(its)
#'
#' interestMeasure(its, transactions = Income)
setGeneric("interestMeasure",
function(x,
measure,
transactions = NULL,
reuse = TRUE,
...)
standardGeneric("interestMeasure"))
### only used to create the manual pages
addl_doc <- list(
chisquared = "Additional parameters are: `significance = TRUE` returns the p-value of the test for independence instead of the chi-squared statistic. For p-values, substitution effects (the occurrence of one item makes the occurrence of another item less likely) can be tested using the parameter `complements = FALSE`. Note: Correction for multiple comparisons can be done using [stats::p.adjust()].",
fishersexacttest = "By default complementary effects are mined, substitutes can be found by using the parameter `complements = FALSE`. Note that Fisher's exact test is equal to hyper-confidence with `significance = TRUE`. Correction for multiple comparisons can be done using [stats::p.adjust()].",
hyperconfidence = "Reports the confidence level by default and the significance level if `significance = TRUE` is used. By default complementary effects are mined, substitutes (too low co-occurrence counts) can be found by using the parameter `complements = FALSE`.",
hyperlift = "The used quantile can be changed using parameter `level` (default: `level = 0.99`).",
laplace = "Parameter `k` can be used to specify the number of classes (default is 2).",
stdLift = "By default, standardized lift is is corrected for minimum support and minimum confidence. Correction can be disabled by using the argument `correct = FALSE`.",
table = "Returns the four counts for the contingency table. The entries are labeled `n11`, `n01`, `n10`, and `n00` (the first subscript is for X and the second is for Y; 1 indicated presence and 0 indicates absence). If several measures are specified, then the counts have the prefix `table.`"
)
create_measures_doc <- function(measures) {
measures_doc <-
grep("^## ", readLines("docs/measures.md"), value = TRUE)
measures_doc <-
do.call(rbind,
strsplit(measures_doc, '\\s*\\{\\s*|##\\s*|#|\\s*\\}\\s*'))[, c(2, 4)]
m <- match(tolower(measures), measures_doc[, 2])
if (any(is.na(m)))
warning("No matching entry for measure: ",
paste(measures[is.na(m)], collapse = ", "))
mm <- cbind(measures_doc[m, ], measures)
paste0(
'* "',
mm[, 3],
'": [',
mm[, 1],
'.](https://mhahsler.github.io/arules/docs/measures#',
mm[, 2],
') ',
sapply(
tolower(mm[, 3]),
FUN = function(m)
if (!is.null(addl_doc[[m]]))
addl_doc[[m]]
else
""
),
collapse = "\n"
)
}
measuresItemsets <- c("support",
"count",
"allConfidence",
"crossSupportRatio",
"lift")
#' @rdname interestMeasure
setMethod("interestMeasure", signature(x = "itemsets"),
function(x,
measure,
transactions = NULL,
reuse = TRUE,
...) {
## backward comp.
add <- list(...)
if (!is.null(add$method)) {
warning("interestMeasure: parameter method is now deprecated! Use measure instead!")
measure <- add$method
}
if (missing(measure))
measure <- measuresItemsets
## check and expand measure
if (any(is.na(ind <- pmatch(
tolower(measure),
tolower(measuresItemsets)
))))
stop(
gettextf(
"Invalid measure(s) for rules: %s",
paste(measure[is.na(ind)], collapse = ", ")
),
gettextf(
"\n\n Available measures: %s",
paste(measuresItemsets, collapse = ", ")
),
domain = NA
)
measure <- measuresItemsets[ind]
## remove quality information if we do not want to reuse! Then we can start reusing
if (!reuse)
quality(x) <- data.frame(seq_len(length(x)))[, 0]
if (is.null(quality(x)[["support"]]))
quality(x)[["support"]] <-
support(x, transactions = transactions)
## deal with multiple measures
if (length(measure) > 1)
return(as.data.frame(
sapply(
measure,
FUN =
function(m)
interestMeasure(x, m, transactions, reuse = TRUE, ...),
USE.NAMES = TRUE,
simplify = FALSE
)
))
## first see if we already have it:
if (!is.null(quality(x)[[measure]]))
return(quality(x)[[measure]])
## calculate measures
if (measure == "count")
return(round(quality(x)[["support"]] * .getN(x, transactions)))
## all other measures are basic measures
return(.basicItemsetMeasures(x, measure, transactions, ...))
})
.getN <- function(x, transactions) {
## find N from associations or if not available then from transactions
n <- info(x)$ntransactions
if (is.null(n)) {
if (is.null(transactions))
stop("transaction data needed. Please specify the transactions used to mine the itemsets!")
n <- length(transactions)
}
n
}
.basicItemsetMeasures <- function(x,
measure,
transactions = NULL,
...) {
if (is.null(transactions))
stop("transaction data needed. Please specify the transactions used to mine the itemsets!")
itemSupport <- itemFrequency(transactions)
if (length(itemSupport) != nitems(items(x)))
stop("number of items in itemsets and transactions do not match.")
## create an itemset list
itemset_list <- LIST(items(x), decode = FALSE)
## catch empty itemset
if (length(itemset_list) < 1)
return(numeric(0))
## calculate all-confidence for existing (frequent) itemsets.
##
## Edward R. Omiecinski. Alternative interest measures for mining
## associations in databases. IEEE Transactions on Knowledge and
## Data Engineering, 15(1):57-69, Jan/Feb 2003.
##
## calculate all-confidence using itemsets support and the
## singleton support of the most frequent item in the itemset
## all-confidence(Z) = supp(Z) / max(supp(i elem Z))
if (measure == "allConfidence") {
m <-
quality(x)[["support"]] / sapply(itemset_list, function(i)
max(itemSupport[i]))
### deal with 1-itemsets
is1 <- size(x) == 1
m[is1] <- quality(x)[["support"]][is1]
}
## calculate the cross-support ratio
## used to eliminate cross support patterns which contain item with
## extremely different support. These patterns tend to be spurious
## (i.e., one item which occurs in virtually all transactions and some very
## rare items)
##
## Hui Xiong, Pang-Ning Tan, Vipin Kumar. Mining Strong Affinity Association
## Patterns in Data Sets with Skewed Support. Third IEEE International
## Conference on Data Mining, Melbourne, Florida, November 19 - 22, 2003.
##
if (measure == "crossSupportRatio")
m <- sapply(itemset_list, function(i)
min(itemSupport[i])) /
sapply(itemset_list, function(i)
max(itemSupport[i]))
if (measure == "lift")
m <-
quality(x)[["support"]] / sapply(itemset_list, function(i)
prod(itemSupport[i]))
m[!is.finite(m)] <- NA
return(m)
}
## measures for rules
# sort measures (except 1-4)
# dput(c(measuresRules[1:4], sort(measuresRules[-(1:4)])))
measuresRules <-
c(
"support",
"confidence",
"lift",
"count",
"addedValue",
"boost",
"casualConfidence",
"casualSupport",
"centeredConfidence",
"certainty",
"chiSquared",
"collectiveStrength",
"confirmedConfidence",
"conviction",
"cosine",
"counterexample",
"coverage",
"doc",
"fishersExactTest",
"gini",
"hyperConfidence",
"hyperLift",
"imbalance",
"implicationIndex",
"importance",
"improvement",
"jaccard",
"jMeasure",
"kappa",
"kulczynski",
"lambda",
"laplace",
"leastContradiction",
"lerman",
"leverage",
"maxconfidence",
"mutualInformation",
"oddsRatio",
"phi",
"ralambondrainy",
"relativeRisk",
"rhsSupport",
"RLD",
"rulePowerFactor",
"sebag",
"stdLift",
"table",
"varyingLiaison",
"yuleQ",
"yuleY"
)
#' @rdname interestMeasure
setMethod("interestMeasure", signature(x = "rules"),
function(x,
measure,
transactions = NULL,
reuse = TRUE,
...) {
## backward comp.
add <- list(...)
if (!is.null(add$method)) {
warning("interestMeasure: parameter method is now deprecated! Use measure instead!")
measure <- add$method
}
if (missing(measure))
measure <- measuresRules
## check and expand measure
if (any(is.na(ind <- pmatch(
tolower(measure),
tolower(measuresRules)
))))
stop(
gettextf(
"Invalid measure(s) for rules: %s",
paste(measure[is.na(ind)], collapse = ", ")
),
gettextf(
"\n\n Available measures: %s",
paste(measuresRules, collapse = ", ")
),
domain = NA
)
measure <- measuresRules[ind]
## remove quality information if we do not want to reuse! Then we can start reusing
if (!reuse)
quality(x) <- data.frame(seq_len(length(x)))[, 0]
## precalculate some measures once (most measures can be calculated using support, confidence, and lift)
## if we haive no support then we probably have nothing! Count it with a single p-tree
if (is.null(quality(x)[["support"]])) {
s <-
support(c(items(x), lhs(x), rhs(x)), transactions = transactions)
quality(x)[["support"]] <- s[seq(length(x))]
quality(x)[["coverage"]] <- s[length(x) + seq(length(x))]
quality(x)[["confidence"]] <-
quality(x)[["support"]] / quality(x)[["coverage"]]
quality(x)[["rhsSupport"]] <-
s[2 * length(x) + seq(length(x))]
quality(x)[["lift"]] <-
quality(x)[["confidence"]] / quality(x)[["rhsSupport"]]
}
if (is.null(quality(x)[["coverage"]]))
quality(x)[["coverage"]] <-
coverage(x, transactions = transactions)
if (is.null(quality(x)[["confidence"]]))
quality(x)[["confidence"]] <-
quality(x)[["support"]] / quality(x)[["coverage"]]
if (is.null(quality(x)[["lift"]]))
quality(x)[["lift"]] <-
quality(x)[["confidence"]] / .rhsSupport(x, transactions = transactions)
if (length(measure) > 1L)
return(as.data.frame(
sapply(
measure,
FUN =
function(m)
interestMeasure(x, m, transactions = transactions, reuse = TRUE, ...),
USE.NAMES = TRUE,
simplify = FALSE
)
))
## catch empty ruleset
if (length(x) < 1)
return(numeric(0))
## first see if we already have a basic measure. All others we recalculate.
if (measure %in% c("support", "confidence", "coverage", "lift")
&& !is.null(quality(x)[[measure]]))
return(quality(x)[[measure]])
## calculate measure (support, confidence, lift and coverage are already handled)
if (measure == "boost")
return(.conf_boost(x, transactions = transactions, ...))
if (measure == "count")
return(round(quality(x)[["support"]] * .getN(x, transactions)))
if (measure == "rhsSupport")
return(.rhsSupport(x, transactions))
if (measure == "improvement")
return(.improvement(x, transactions = transactions, ...))
if (measure == "hyperLift")
return(.hyperLift(x, transactions = transactions, ...))
if (measure == "hyperConfidence")
return(.hyperConfidence(x, transactions = transactions, ...))
if (measure == "fishersExactTest")
return(.hyperConfidence(x,
transactions = transactions,
significance = TRUE,
...))
if (measure == "RLD")
return(.RLD(x, transactions = transactions, ...))
if (measure == "stdLift")
return(.stdLift(x, transactions = transactions, ...))
## all other measures are implemented here (i is in ...)
ret <-
.basicRuleMeasure(x, measure, transactions = transactions, ...)
## make all bad values NA (does not work for measures that return data.frames)
#if (is.vector(ret)) ret[!is.finite(ret)] <- NA
return(ret)
stop("Specified measure not implemented.")
})
## calculate hyperlift for existing rules.
##
## Michael Hahsler, Kurt Hornik, and Thomas Reutterer.
## Implications of probabilistic data modeling for rule mining.
## Report 14, Research Report Series, Department of Statistics and
## Mathematics, Wirtschaftsuniversitaet Wien, Augasse 2-6, 1090 Wien,
## Austria, March 2005.
## hyperlift(X => Y) = c_X,Y / Q_d[C_X,Y]
##
## where Q_d[C_X,Y] = qhyper(d, m = c_Y, n = length(trans.) - c_Y, k = c_X)
##
## c_X,Y = count(X => Y)
## c_X = count(X)
## c_Y = count(Y)
##
## this implements only hyperlift for rules with a single item in the consequent
.hyperLift <- function(x, level = 0.99, ...) {
counts <- .getCounts(x, ...)
with(counts, {
Q <-
stats::qhyper(
level,
m = nx1,
n = n - nx1,
k = n1x,
lower.tail = TRUE
)
n11 / Q
})
}
## calculate hyperconfidence for existing rules.
## (confidence level that we observe too high/low counts)
##
## uses the model from:
## Hahsler, Michael and Kurt Hornik (2007). New probabilistic
## interest measures for association rules.
## Intelligent Data Analysis, 11(5):437--455.
.hyperConfidence <-
function(x,
complements = TRUE,
significance = FALSE,
...) {
## significance: return significance levels instead of
## confidence levels
counts <- .getCounts(x, ...)
if (complements == TRUE)
## c_XY - 1 so we get P[C_XY < c_XY] instead of P[C_XY <= c_XY]
res <- with(counts, {
stats::phyper(
n11 - 1,
m = nx1,
n = n - nx1,
k = n1x,
lower.tail = !significance
)
})
else
## substitutes; Pr[C_XY > c_XY]
## empty LHS causes a div by zero -> NAN
suppressWarnings(res <- with(counts, {
stats::phyper(
n11,
m = nx1,
n = n - n1x,
k = n1x,
lower.tail = significance
)
}))
res[is.nan(res)] <- NA
res
}
## Minimum Improvement (Bayardo et al. 1999)
## Let the improvement of a rule be defined as the minimum
## difference between its confidence and the confidence of any
## proper sub-rule with the same consequent.
.improvement <- function(x,
improvementMeasure = "confidence", ...) {
## Note: improvement is defined for confidence, but could also used with
## other measures
q <- interestMeasure(x, measure = improvementMeasure, ...)
imp <- numeric(length(x))
### do it by unique rhs
rr <- .Call(R_pnindex, rhs(x)@data, NULL, FALSE)
for (r in unique(rr)) {
pos <- which(rr == r)
q2 <- q[pos]
### FALSE is for verbose
qsubmax <- .Call(R_pnmax, lhs(x[pos])@data, q2, FALSE)
imp[pos] <- q2 - qsubmax
}
imp
}
.conf_boost <- function(x, ...) {
conf <- interestMeasure(x, "confidence", ...)
imp <- .improvement(x, ...)
conf / (conf - imp)
}
.getCounts <-
function(x,
transactions = NULL,
reuse = TRUE,
smoothCounts = 0) {
if (smoothCounts < 0)
stop("smoothCount needs to be >= 0!")
q <-
interestMeasure(
x,
c("support", "coverage", "rhsSupport"),
transactions = transactions,
reuse = reuse
)
n <- .getN(x, transactions)
n11 <- round(q$support * n)
n1x <- round(q$coverage * n)
nx1 <- round(q$rhsSupport * n)
n0x <- n - n1x
nx0 <- n - nx1
n10 <- n1x - n11
n01 <- nx1 - n11
n00 <- n0x - n01
if (smoothCounts > 0) {
n <- n + 4 * smoothCounts
n11 <- n11 + smoothCounts
n10 <- n10 + smoothCounts
n01 <- n01 + smoothCounts
n00 <- n00 + smoothCounts
n0x <- n0x + 2 * smoothCounts
nx0 <- nx0 + 2 * smoothCounts
n1x <- n1x + 2 * smoothCounts
nx1 <- nx1 + 2 * smoothCounts
}
list(
n11 = n11,
n01 = n01,
n10 = n10,
n00 = n00,
n1x = n1x,
nx1 = nx1,
n0x = n0x,
nx0 = nx0,
n = n
)
}
.rhsSupport <- function(x, transactions) {
q <- quality(x)
if (!is.null(q$confidence) && !is.null(q$lift)) {
rhsSupport <- q$confidence / q$lift
### in case lift was NaN (0/0)
rhsSupport[is.na(rhsSupport)] <- 0
} else {
if (is.null(transactions))
stop(
"transactions missing. Please specify the data used to mine the rules as transactions!"
)
if (all(diff(rhs(x)@data@p) == 1))
### this is a lot faster for single items in the RHS
rhsSupport <-
unname(itemFrequency(transactions)[rhs(x)@data@i + 1L])
else
rhsSupport <-
support(rhs(x), transactions) ### multiple items in the RHS
}
return(rhsSupport)
}
## More measures (see Tan et al. Introduction to Data Mining, 2006)
# x can be a set of rules or list with counts (at least n11, n10, n01, n11 and n)
.basicRuleMeasure <- function(x,
measure,
transactions = NULL,
### adds smoothCounts to the count in each cell to avoid counts of 0
smoothCounts = 0,
### k is the number of classes used by laplace
significance = FALSE,
### used by chi-squared
compliment = TRUE,
### k is the number of classes used by laplace
k = 2) {
if (is(x, "rules"))
counts <-
.getCounts(x, transactions, smoothCounts = smoothCounts)
else {
# is counts a matrix?
if (is.matrix(x)) {
counts <- lapply(seq_len(ncol(x)), function(i)
x[, i])
names(counts) <- colnames(x)
} else
counts <- x
# complete missing counts if x has counts
if (is.null(counts$n))
counts$n <- counts$n11 + counts$n10 + counts$n01 + counts$n00
if (is.null(counts$n1x))
counts$n1x <- counts$n11 + counts$n10
if (is.null(counts$nx1))
counts$nx1 <- counts$n11 + counts$n01
if (is.null(counts$n0x))
counts$n0x <- counts$n - counts$n1x
if (is.null(counts$nx0))
counts$nx0 <- counts$n - counts$nx1
}
# note return in with just assigns to m
m <- with(counts,
switch(
measure,
table = data.frame(
n11 = n11,
n01 = n01,
n10 = n10,
n00 = n00
),
support = n11 / n,
confidence = n11 / n1x,
lift = n * n11 / (n1x * nx1),
coverage = n1x / n,
rhsSupport = nx1 / n,
cosine = n11 / sqrt(n1x * nx1),
conviction = n1x * nx0 / (n * n10),
gini = n1x / n * ((n11 / n1x) ^ 2 + (n10 / n1x) ^ 2) - (nx1 / n) ^ 2 +
n0x / n * ((n01 / n0x) ^ 2 + (n00 / n0x) ^ 2) - (nx0 / n) ^ 2,
rulePowerFactor = n11 * n11 / n1x / n,
oddsRatio = n11 * n00 / (n10 * n01),
relativeRisk = (n11 / n1x) / (n01 / n0x),
phi = (n * n11 - n1x * nx1) / sqrt(n1x * nx1 * n0x * nx0),
leverage = n11 / n - (n1x * nx1 / n ^ 2),
collectiveStrength = n11 * n00 / (n1x * nx1 + n0x + nx0) *
(n ^ 2 - n1x * nx1 - n0x * nx0) / (n - n11 - n00),
importance = log(((n11 + 1) * (n0x + 2)) / ((n01 + 1) * (n1x + 2)), base = 10),
imbalance = abs(n1x - nx1) / (n1x + nx1 - n11),
jaccard = n11 / (n1x + nx1 - n11),
kappa = (n * n11 + n * n00 - n1x * nx1 - n0x * nx0) / (n ^ 2 - n1x * nx1 -
n0x * nx0),
lambda = {
max_x0x1 <- apply(cbind(nx1, nx0), 1, max)
(apply(cbind(n11, n10), 1, max) + apply(cbind(n01, n00), 1, max) -
max_x0x1) / (n - max_x0x1)
},
mutualInformation = (
n00 / n * log(n * n00 / (n0x * nx0)) +
n01 / n * log(n * n01 / (n0x * nx1)) +
n10 / n * log(n * n10 / (n1x * nx0)) +
n11 / n * log(n * n11 / (n1x * nx1))
) /
pmin(-1 * (n0x / n * log(n0x / n) + n1x / n * log(n1x / n)), -1 *
(nx0 / n * log(nx0 / n) + nx1 / n * log(nx1 / n))),
maxconfidence = pmax(n11 / n1x, n11 / nx1),
jMeasure = n11 / n * log(n * n11 / (n1x * nx1)) +
n10 / n * log(n * n10 / (n1x * nx0)),
kulczynski = (n11 / n1x + n11 / nx1) / 2,
laplace = (n11 + 1) / (n1x + k),
certainty = (n11 / n1x - nx1 / n) / (1 - nx1 / n),
addedValue = n11 / n1x - nx1 / n,
ralambondrainy = n10 / n,
sebag = (n1x - n10) / n10,
counterexample = (n11 - n10) / n11,
# needs alpha
#if(measure == "wang") return(1/n * (1-alpha) * n1x - n10)
confirmedConfidence = (n11 - n10) / n1x,
casualSupport = (n1x + nx1 - 2 * n10) / n,
casualConfidence = 1 - n10 / n * (1 / n1x + 1 / nx1),
leastContradiction = (n1x - n10) / nx1,
centeredConfidence = nx0 / n - n10 / n1x,
varyingLiaison = (n1x - n10) / (n1x * nx1 / n) - 1,
yuleQ = {
OR <- n11 * n00 / (n10 * n01)
(OR - 1) / (OR + 1)
},
yuleY = {
OR <- n11 * n00 / (n10 * n01)
(sqrt(OR) - 1) / (sqrt(OR) + 1)
},
lerman = (n11 - n1x * nx1 / n) / sqrt(n1x * nx1 / n),
implicationIndex = (n10 - n1x * nx0 / n) / sqrt(n1x * nx0 / n),
doc = (n11 / n1x) - (n01 / n0x),
chiSquared = {
chi2 <- numeric(length(x))
for (i in seq_len(length(x))) {
fo <- matrix(c(n00[i], n01[i], n10[i], n11[i]), ncol = 2)
#fe <- tcrossprod(c(nx0[i], nx1[i]), ic(n0x[i], n1x[i])) / n
## check if approximation is ok
## we don't do this now
##if(any(fe < 5)) chi2[i] <- nA
##else
#chi2[i] <- sum((fo - fe) ^ 2 / fe)
# warning about approximation
suppressWarnings(chi2[i] <-
stats::chisq.test(fo, correct = FALSE)$statistic)
}
## the chi square test has 1 df for a 2x2 contingency table.
## The critical value at alpha=0.05 is:
## qchisq(0.05, df =1, lower.tail=FALSE)
## [1] 3.841459
if (!significance)
chi2
else
stats::pchisq(q = chi2,
df = 1,
lower.tail = !compliment)
}
))
if (is.null(m))
stop("Specified measure not implemented.")
m
}
## RLD see Kenett and Salini 2008
## RLD code contributed by Silvia Salini
.RLD <- function(x, ...) {
counts <- .getCounts(x, ...)
RLD <- with(counts, {
RLD <- numeric(length(x))
for (i in seq_len(length(x))) {
D <- (n11[i] * n00[i] - n10[i] * n01[i]) / n
if (D > 0)
if (n01[i] < n10[i])
RLD[i] <- D / (D + n01[i])
else
RLD[i] <- D / (D + n10[i])
else
if (n11[i] < n00[i])
RLD[i] <- D / (D - n11[i])
else
RLD[i] <- D / (D - n00[i])
}
RLD
})
RLD[!is.finite(RLD)] <- NA
RLD
}
## Standardising the Lift of an Association Rule
# by McNicholas, 2008, DOI: 10.1016/j.csda.2008.03.013
.stdLift <-
function(rules,
transactions = NULL,
correct = TRUE) {
measures <- interestMeasure(rules,
c("support", "confidence", "lift", "coverage", "rhsSupport"),
transactions = transactions)
n <- info(rules)$ntransactions
if (is.null(n)) {
if (is.null(transactions))
stop("rules do not contain info from transactions. transactions are needed.")
n <- length(transactions)
}
supp_A <- measures$coverage
supp_B <- measures$rhsSupport
# upper bound of lift
lambda <- pmax(supp_A + supp_B - 1, 1 / n) / (supp_A * supp_B)
# correct lambda for min. confidence and min. support
if (correct) {
c <- info(rules)$confidence
s <- info(rules)$support
if (!is.null(c) && !is.null(s))
lambda <-
pmax(lambda, 4 * s / (1 + s) ^ 2, s / (supp_A * supp_B), c / supp_B)
else
warning(
"minimum support or confidence not available in info(x). Using uncorrected stdLift instead."
)
}
# lower bound of lift
upsilon <- 1 / pmax(supp_A, supp_B)
stdLift <- (measures$lift - lambda) / (upsilon - lambda)
stdLift[is.nan(stdLift)] <- 1
stdLift
}
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Defines functions .stdLift .RLD .basicRuleMeasure .rhsSupport .getCounts .conf_boost .improvement .hyperConfidence .hyperLift .basicItemsetMeasures .getN create_measures_doc
#######################################################################
# arules - Mining Association Rules and Frequent Itemsets
# Copyright (C) 2011-2015 Michael Hahsler, Christian Buchta,
# Bettina Gruen and Kurt Hornik
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
#' Calculate Additional Interest Measures
#'
#' Provides the generic function `interestMeasure()` and the
#' methods to calculate various additional interest measures for existing sets
#' of [itemsets] or [rules].
#'
#' A searchable list of definitions, equations and
#' references for all available interest measures can be found at
#' \url{https://mhahsler.github.io/arules/docs/measures}. The descriptions
#' are also linked in the list below.
#'
#' The following measures are implemented for **itemsets**:
#' `r arules:::create_measures_doc(arules:::measuresItemsets)`
#'
#' The following measures are implemented for **rules**:
#' `r arules:::create_measures_doc(arules:::measuresRules)`
#'
#' @aliases interestMeasure
#' @family interest measures
#'
#' @param x a set of [itemsets] or [rules].
#' @param measure name or vector of names of the desired interest measures (see
#' the Details section for available measures). If measure is missing then all available
#' measures are calculated.
#' @param transactions the [transactions] used to mine the associations
#' or a set of different transactions to calculate interest measures from
#' (Note: you need to set `reuse = FALSE` in the later case).
#' @param reuse logical indicating if information in the quality slot should be
#' reuse for calculating the measures. This speeds up the process significantly
#' since only very little (or no) transaction counting is necessary if support,
#' confidence and lift are already available. Use `reuse = FALSE` to force
#' counting (might be very slow but is necessary if you use a different set of
#' transactions than was used for mining).
#' @param ... further arguments for the measure calculation. Many measures
#' are based on contingency table counts and zero counts can produce `NaN` values
#' (division by zero). This issue can be resolved by using the additional
#' parameter `smoothCounts` which performs additive smoothing by adds a
#' "pseudo count" of `smoothCounts` to each cell in the contingency
#' table. Use `smoothCounts = 1` or larger values for Laplace smoothing.
#' Use `smoothCounts = .5` for Haldane-Anscombe correction (Haldan, 1940; Anscombe, 1956)
#' which is often used for chi-squared, phi correlation and related measures.
#'
#' @return If only one measure is used, the function returns a numeric vector
#' containing the values of the interest measure for each association in the
#' set of associations `x`.
#'
#' If more than one measures are specified, the result is a data.frame
#' containing the different measures for each association as columns.
#'
#' `NA` is returned for rules/itemsets for which a certain measure is not
#' defined.
#' @author Michael Hahsler
#' @seealso [itemsets], [rules]
#' @references
#' Hahsler, Michael (2015). A Probabilistic Comparison of Commonly Used
#' Interest Measures for Association Rules, 2015, URL:
#' \url{https://mhahsler.github.io/arules/docs/measures}.
#'
#' Haldane, J.B.S. (1940). "The mean and variance of the moments of chi-squared
#' when used as a test of homogeneity, when expectations are small".
#' _Biometrika,_ 29, 133-134.
#'
#' Anscombe, F.J. (1956). "On estimating binomial response relations".
#' _Biometrika,_ 43, 461-464.
#' @keywords models
#' @examples
#'
#' data("Income")
#' rules <- apriori(Income)
#'
#' ## calculate a single measure and add it to the quality slot
#' quality(rules) <- cbind(quality(rules),
#' hyperConfidence = interestMeasure(rules, measure = "hyperConfidence",
#' transactions = Income))
#'
#' inspect(head(rules, by = "hyperConfidence"))
#'
#' ## calculate several measures
#' m <- interestMeasure(rules, c("confidence", "oddsRatio", "leverage"),
#' transactions = Income)
#' inspect(head(rules))
#' head(m)
#'
#' ## calculate all available measures for the first 5 rules and show them as a
#' ## table with the measures as rows
#' t(interestMeasure(head(rules, 5), transactions = Income))
#'
#' ## calculate measures on a different set of transactions (I use a sample here)
#' ## Note: reuse = TRUE (default) would just return the stored support on the
#' ## data set used for mining
#' newTrans <- sample(Income, 100)
#' m2 <- interestMeasure(rules, "support", transactions = newTrans, reuse = FALSE)
#' head(m2)
#'
#' ## calculate all available measures for the 5 frequent itemsets with highest support
#' its <- apriori(Income, parameter = list(target = "frequent itemsets"))
#' its <- head(its, 5, by = "support")
#' inspect(its)
#'
#' interestMeasure(its, transactions = Income)
setGeneric("interestMeasure",
function(x,
measure,
transactions = NULL,
reuse = TRUE,
...)
standardGeneric("interestMeasure"))
### only used to create the manual pages
addl_doc <- list(
chisquared = "Additional parameters are: `significance = TRUE` returns the p-value of the test for independence instead of the chi-squared statistic. For p-values, substitution effects (the occurrence of one item makes the occurrence of another item less likely) can be tested using the parameter `complements = FALSE`. Note: Correction for multiple comparisons can be done using [stats::p.adjust()].",
fishersexacttest = "By default complementary effects are mined, substitutes can be found by using the parameter `complements = FALSE`. Note that Fisher's exact test is equal to hyper-confidence with `significance = TRUE`. Correction for multiple comparisons can be done using [stats::p.adjust()].",
hyperconfidence = "Reports the confidence level by default and the significance level if `significance = TRUE` is used. By default complementary effects are mined, substitutes (too low co-occurrence counts) can be found by using the parameter `complements = FALSE`.",
hyperlift = "The used quantile can be changed using parameter `level` (default: `level = 0.99`).",
laplace = "Parameter `k` can be used to specify the number of classes (default is 2).",
stdLift = "By default, standardized lift is is corrected for minimum support and minimum confidence. Correction can be disabled by using the argument `correct = FALSE`.",
table = "Returns the four counts for the contingency table. The entries are labeled `n11`, `n01`, `n10`, and `n00` (the first subscript is for X and the second is for Y; 1 indicated presence and 0 indicates absence). If several measures are specified, then the counts have the prefix `table.`"
)
create_measures_doc <- function(measures) {
measures_doc <-
grep("^## ", readLines("docs/measures.md"), value = TRUE)
measures_doc <-
do.call(rbind,
strsplit(measures_doc, '\\s*\\{\\s*|##\\s*|#|\\s*\\}\\s*'))[, c(2, 4)]
m <- match(tolower(measures), measures_doc[, 2])
if (any(is.na(m)))
warning("No matching entry for measure: ",
paste(measures[is.na(m)], collapse = ", "))
mm <- cbind(measures_doc[m, ], measures)
paste0(
'* "',
mm[, 3],
'": [',
mm[, 1],
'.](https://mhahsler.github.io/arules/docs/measures#',
mm[, 2],
') ',
sapply(
tolower(mm[, 3]),
FUN = function(m)
if (!is.null(addl_doc[[m]]))
addl_doc[[m]]
else
""
),
collapse = "\n"
)
}
measuresItemsets <- c("support",
"count",
"allConfidence",
"crossSupportRatio",
"lift")
#' @rdname interestMeasure
setMethod("interestMeasure", signature(x = "itemsets"),
function(x,
measure,
transactions = NULL,
reuse = TRUE,
...) {
## backward comp.
add <- list(...)
if (!is.null(add$method)) {
warning("interestMeasure: parameter method is now deprecated! Use measure instead!")
measure <- add$method
}
if (missing(measure))
measure <- measuresItemsets
## check and expand measure
if (any(is.na(ind <- pmatch(
tolower(measure),
tolower(measuresItemsets)
))))
stop(
gettextf(
"Invalid measure(s) for rules: %s",
paste(measure[is.na(ind)], collapse = ", ")
),
gettextf(
"\n\n Available measures: %s",
paste(measuresItemsets, collapse = ", ")
),
domain = NA
)
measure <- measuresItemsets[ind]
## remove quality information if we do not want to reuse! Then we can start reusing
if (!reuse)
quality(x) <- data.frame(seq_len(length(x)))[, 0]
if (is.null(quality(x)[["support"]]))
quality(x)[["support"]] <-
support(x, transactions = transactions)
## deal with multiple measures
if (length(measure) > 1)
return(as.data.frame(
sapply(
measure,
FUN =
function(m)
interestMeasure(x, m, transactions, reuse = TRUE, ...),
USE.NAMES = TRUE,
simplify = FALSE
)
))
## first see if we already have it:
if (!is.null(quality(x)[[measure]]))
return(quality(x)[[measure]])
## calculate measures
if (measure == "count")
return(round(quality(x)[["support"]] * .getN(x, transactions)))
## all other measures are basic measures
return(.basicItemsetMeasures(x, measure, transactions, ...))
})
.getN <- function(x, transactions) {
## find N from associations or if not available then from transactions
n <- info(x)$ntransactions
if (is.null(n)) {
if (is.null(transactions))
stop("transaction data needed. Please specify the transactions used to mine the itemsets!")
n <- length(transactions)
}
n
}
.basicItemsetMeasures <- function(x,
measure,
transactions = NULL,
...) {
if (is.null(transactions))
stop("transaction data needed. Please specify the transactions used to mine the itemsets!")
itemSupport <- itemFrequency(transactions)
if (length(itemSupport) != nitems(items(x)))
stop("number of items in itemsets and transactions do not match.")
## create an itemset list
itemset_list <- LIST(items(x), decode = FALSE)
## catch empty itemset
if (length(itemset_list) < 1)
return(numeric(0))
## calculate all-confidence for existing (frequent) itemsets.
##
## Edward R. Omiecinski. Alternative interest measures for mining
## associations in databases. IEEE Transactions on Knowledge and
## Data Engineering, 15(1):57-69, Jan/Feb 2003.
##
## calculate all-confidence using itemsets support and the
## singleton support of the most frequent item in the itemset
## all-confidence(Z) = supp(Z) / max(supp(i elem Z))
if (measure == "allConfidence") {
m <-
quality(x)[["support"]] / sapply(itemset_list, function(i)
max(itemSupport[i]))
### deal with 1-itemsets
is1 <- size(x) == 1
m[is1] <- quality(x)[["support"]][is1]
}
## calculate the cross-support ratio
## used to eliminate cross support patterns which contain item with
## extremely different support. These patterns tend to be spurious
## (i.e., one item which occurs in virtually all transactions and some very
## rare items)
##
## Hui Xiong, Pang-Ning Tan, Vipin Kumar. Mining Strong Affinity Association
## Patterns in Data Sets with Skewed Support. Third IEEE International
## Conference on Data Mining, Melbourne, Florida, November 19 - 22, 2003.
##
if (measure == "crossSupportRatio")
m <- sapply(itemset_list, function(i)
min(itemSupport[i])) /
sapply(itemset_list, function(i)
max(itemSupport[i]))
if (measure == "lift")
m <-
quality(x)[["support"]] / sapply(itemset_list, function(i)
prod(itemSupport[i]))
m[!is.finite(m)] <- NA
return(m)
}
## measures for rules
# sort measures (except 1-4)
# dput(c(measuresRules[1:4], sort(measuresRules[-(1:4)])))
measuresRules <-
c(
"support",
"confidence",
"lift",
"count",
"addedValue",
"boost",
"casualConfidence",
"casualSupport",
"centeredConfidence",
"certainty",
"chiSquared",
"collectiveStrength",
"confirmedConfidence",
"conviction",
"cosine",
"counterexample",
"coverage",
"doc",
"fishersExactTest",
"gini",
"hyperConfidence",
"hyperLift",
"imbalance",
"implicationIndex",
"importance",
"improvement",
"jaccard",
"jMeasure",
"kappa",
"kulczynski",
"lambda",
"laplace",
"leastContradiction",
"lerman",
"leverage",
"maxconfidence",
"mutualInformation",
"oddsRatio",
"phi",
"ralambondrainy",
"relativeRisk",
"rhsSupport",
"RLD",
"rulePowerFactor",
"sebag",
"stdLift",
"table",
"varyingLiaison",
"yuleQ",
"yuleY"
)
#' @rdname interestMeasure
setMethod("interestMeasure", signature(x = "rules"),
function(x,
measure,
transactions = NULL,
reuse = TRUE,
...) {
## backward comp.
add <- list(...)
if (!is.null(add$method)) {
warning("interestMeasure: parameter method is now deprecated! Use measure instead!")
measure <- add$method
}
if (missing(measure))
measure <- measuresRules
## check and expand measure
if (any(is.na(ind <- pmatch(
tolower(measure),
tolower(measuresRules)
))))
stop(
gettextf(
"Invalid measure(s) for rules: %s",
paste(measure[is.na(ind)], collapse = ", ")
),
gettextf(
"\n\n Available measures: %s",
paste(measuresRules, collapse = ", ")
),
domain = NA
)
measure <- measuresRules[ind]
## remove quality information if we do not want to reuse! Then we can start reusing
if (!reuse)
quality(x) <- data.frame(seq_len(length(x)))[, 0]
## precalculate some measures once (most measures can be calculated using support, confidence, and lift)
## if we haive no support then we probably have nothing! Count it with a single p-tree
if (is.null(quality(x)[["support"]])) {
s <-
support(c(items(x), lhs(x), rhs(x)), transactions = transactions)
quality(x)[["support"]] <- s[seq(length(x))]
quality(x)[["coverage"]] <- s[length(x) + seq(length(x))]
quality(x)[["confidence"]] <-
quality(x)[["support"]] / quality(x)[["coverage"]]
quality(x)[["rhsSupport"]] <-
s[2 * length(x) + seq(length(x))]
quality(x)[["lift"]] <-
quality(x)[["confidence"]] / quality(x)[["rhsSupport"]]
}
if (is.null(quality(x)[["coverage"]]))
quality(x)[["coverage"]] <-
coverage(x, transactions = transactions)
if (is.null(quality(x)[["confidence"]]))
quality(x)[["confidence"]] <-
quality(x)[["support"]] / quality(x)[["coverage"]]
if (is.null(quality(x)[["lift"]]))
quality(x)[["lift"]] <-
quality(x)[["confidence"]] / .rhsSupport(x, transactions = transactions)
if (length(measure) > 1L)
return(as.data.frame(
sapply(
measure,
FUN =
function(m)
interestMeasure(x, m, transactions = transactions, reuse = TRUE, ...),
USE.NAMES = TRUE,
simplify = FALSE
)
))
## catch empty ruleset
if (length(x) < 1)
return(numeric(0))
## first see if we already have a basic measure. All others we recalculate.
if (measure %in% c("support", "confidence", "coverage", "lift")
&& !is.null(quality(x)[[measure]]))
return(quality(x)[[measure]])
## calculate measure (support, confidence, lift and coverage are already handled)
if (measure == "boost")
return(.conf_boost(x, transactions = transactions, ...))
if (measure == "count")
return(round(quality(x)[["support"]] * .getN(x, transactions)))
if (measure == "rhsSupport")
return(.rhsSupport(x, transactions))
if (measure == "improvement")
return(.improvement(x, transactions = transactions, ...))
if (measure == "hyperLift")
return(.hyperLift(x, transactions = transactions, ...))
if (measure == "hyperConfidence")
return(.hyperConfidence(x, transactions = transactions, ...))
if (measure == "fishersExactTest")
return(.hyperConfidence(x,
transactions = transactions,
significance = TRUE,
...))
if (measure == "RLD")
return(.RLD(x, transactions = transactions, ...))
if (measure == "stdLift")
return(.stdLift(x, transactions = transactions, ...))
## all other measures are implemented here (i is in ...)
ret <-
.basicRuleMeasure(x, measure, transactions = transactions, ...)
## make all bad values NA (does not work for measures that return data.frames)
#if (is.vector(ret)) ret[!is.finite(ret)] <- NA
return(ret)
stop("Specified measure not implemented.")
})
## calculate hyperlift for existing rules.
##
## Michael Hahsler, Kurt Hornik, and Thomas Reutterer.
## Implications of probabilistic data modeling for rule mining.
## Report 14, Research Report Series, Department of Statistics and
## Mathematics, Wirtschaftsuniversitaet Wien, Augasse 2-6, 1090 Wien,
## Austria, March 2005.
## hyperlift(X => Y) = c_X,Y / Q_d[C_X,Y]
##
## where Q_d[C_X,Y] = qhyper(d, m = c_Y, n = length(trans.) - c_Y, k = c_X)
##
## c_X,Y = count(X => Y)
## c_X = count(X)
## c_Y = count(Y)
##
## this implements only hyperlift for rules with a single item in the consequent
.hyperLift <- function(x, level = 0.99, ...) {
counts <- .getCounts(x, ...)
with(counts, {
Q <-
stats::qhyper(
level,
m = nx1,
n = n - nx1,
k = n1x,
lower.tail = TRUE
)
n11 / Q
})
}
## calculate hyperconfidence for existing rules.
## (confidence level that we observe too high/low counts)
##
## uses the model from:
## Hahsler, Michael and Kurt Hornik (2007). New probabilistic
## interest measures for association rules.
## Intelligent Data Analysis, 11(5):437--455.
.hyperConfidence <-
function(x,
complements = TRUE,
significance = FALSE,
...) {
## significance: return significance levels instead of
## confidence levels
counts <- .getCounts(x, ...)
if (complements == TRUE)
## c_XY - 1 so we get P[C_XY < c_XY] instead of P[C_XY <= c_XY]
res <- with(counts, {
stats::phyper(
n11 - 1,
m = nx1,
n = n - nx1,
k = n1x,
lower.tail = !significance
)
})
else
## substitutes; Pr[C_XY > c_XY]
## empty LHS causes a div by zero -> NAN
suppressWarnings(res <- with(counts, {
stats::phyper(
n11,
m = nx1,
n = n - n1x,
k = n1x,
lower.tail = significance
)
}))
res[is.nan(res)] <- NA
res
}
## Minimum Improvement (Bayardo et al. 1999)
## Let the improvement of a rule be defined as the minimum
## difference between its confidence and the confidence of any
## proper sub-rule with the same consequent.
.improvement <- function(x,
improvementMeasure = "confidence", ...) {
## Note: improvement is defined for confidence, but could also used with
## other measures
q <- interestMeasure(x, measure = improvementMeasure, ...)
imp <- numeric(length(x))
### do it by unique rhs
rr <- .Call(R_pnindex, rhs(x)@data, NULL, FALSE)
for (r in unique(rr)) {
pos <- which(rr == r)
q2 <- q[pos]
### FALSE is for verbose
qsubmax <- .Call(R_pnmax, lhs(x[pos])@data, q2, FALSE)
imp[pos] <- q2 - qsubmax
}
imp
}
.conf_boost <- function(x, ...) {
conf <- interestMeasure(x, "confidence", ...)
imp <- .improvement(x, ...)
conf / (conf - imp)
}
.getCounts <-
function(x,
transactions = NULL,
reuse = TRUE,
smoothCounts = 0) {
if (smoothCounts < 0)
stop("smoothCount needs to be >= 0!")
q <-
interestMeasure(
x,
c("support", "coverage", "rhsSupport"),
transactions = transactions,
reuse = reuse
)
n <- .getN(x, transactions)
n11 <- round(q$support * n)
n1x <- round(q$coverage * n)
nx1 <- round(q$rhsSupport * n)
n0x <- n - n1x
nx0 <- n - nx1
n10 <- n1x - n11
n01 <- nx1 - n11
n00 <- n0x - n01
if (smoothCounts > 0) {
n <- n + 4 * smoothCounts
n11 <- n11 + smoothCounts
n10 <- n10 + smoothCounts
n01 <- n01 + smoothCounts
n00 <- n00 + smoothCounts
n0x <- n0x + 2 * smoothCounts
nx0 <- nx0 + 2 * smoothCounts
n1x <- n1x + 2 * smoothCounts
nx1 <- nx1 + 2 * smoothCounts
}
list(
n11 = n11,
n01 = n01,
n10 = n10,
n00 = n00,
n1x = n1x,
nx1 = nx1,
n0x = n0x,
nx0 = nx0,
n = n
)
}
.rhsSupport <- function(x, transactions) {
q <- quality(x)
if (!is.null(q$confidence) && !is.null(q$lift)) {
rhsSupport <- q$confidence / q$lift
### in case lift was NaN (0/0)
rhsSupport[is.na(rhsSupport)] <- 0
} else {
if (is.null(transactions))
stop(
"transactions missing. Please specify the data used to mine the rules as transactions!"
)
if (all(diff(rhs(x)@data@p) == 1))
### this is a lot faster for single items in the RHS
rhsSupport <-
unname(itemFrequency(transactions)[rhs(x)@data@i + 1L])
else
rhsSupport <-
support(rhs(x), transactions) ### multiple items in the RHS
}
return(rhsSupport)
}
## More measures (see Tan et al. Introduction to Data Mining, 2006)
# x can be a set of rules or list with counts (at least n11, n10, n01, n11 and n)
.basicRuleMeasure <- function(x,
measure,
transactions = NULL,
### adds smoothCounts to the count in each cell to avoid counts of 0
smoothCounts = 0,
### k is the number of classes used by laplace
significance = FALSE,
### used by chi-squared
compliment = TRUE,
### k is the number of classes used by laplace
k = 2) {
if (is(x, "rules"))
counts <-
.getCounts(x, transactions, smoothCounts = smoothCounts)
else {
# is counts a matrix?
if (is.matrix(x)) {
counts <- lapply(seq_len(ncol(x)), function(i)
x[, i])
names(counts) <- colnames(x)
} else
counts <- x
# complete missing counts if x has counts
if (is.null(counts$n))
counts$n <- counts$n11 + counts$n10 + counts$n01 + counts$n00
if (is.null(counts$n1x))
counts$n1x <- counts$n11 + counts$n10
if (is.null(counts$nx1))
counts$nx1 <- counts$n11 + counts$n01
if (is.null(counts$n0x))
counts$n0x <- counts$n - counts$n1x
if (is.null(counts$nx0))
counts$nx0 <- counts$n - counts$nx1
}
# note return in with just assigns to m
m <- with(counts,
switch(
measure,
table = data.frame(
n11 = n11,
n01 = n01,
n10 = n10,
n00 = n00
),
support = n11 / n,
confidence = n11 / n1x,
lift = n * n11 / (n1x * nx1),
coverage = n1x / n,
rhsSupport = nx1 / n,
cosine = n11 / sqrt(n1x * nx1),
conviction = n1x * nx0 / (n * n10),
gini = n1x / n * ((n11 / n1x) ^ 2 + (n10 / n1x) ^ 2) - (nx1 / n) ^ 2 +
n0x / n * ((n01 / n0x) ^ 2 + (n00 / n0x) ^ 2) - (nx0 / n) ^ 2,
rulePowerFactor = n11 * n11 / n1x / n,
oddsRatio = n11 * n00 / (n10 * n01),
relativeRisk = (n11 / n1x) / (n01 / n0x),
phi = (n * n11 - n1x * nx1) / sqrt(n1x * nx1 * n0x * nx0),
leverage = n11 / n - (n1x * nx1 / n ^ 2),
collectiveStrength = n11 * n00 / (n1x * nx1 + n0x + nx0) *
(n ^ 2 - n1x * nx1 - n0x * nx0) / (n - n11 - n00),
importance = log(((n11 + 1) * (n0x + 2)) / ((n01 + 1) * (n1x + 2)), base = 10),
imbalance = abs(n1x - nx1) / (n1x + nx1 - n11),
jaccard = n11 / (n1x + nx1 - n11),
kappa = (n * n11 + n * n00 - n1x * nx1 - n0x * nx0) / (n ^ 2 - n1x * nx1 -
n0x * nx0),
lambda = {
max_x0x1 <- apply(cbind(nx1, nx0), 1, max)
(apply(cbind(n11, n10), 1, max) + apply(cbind(n01, n00), 1, max) -
max_x0x1) / (n - max_x0x1)
},
mutualInformation = (
n00 / n * log(n * n00 / (n0x * nx0)) +
n01 / n * log(n * n01 / (n0x * nx1)) +
n10 / n * log(n * n10 / (n1x * nx0)) +
n11 / n * log(n * n11 / (n1x * nx1))
) /
pmin(-1 * (n0x / n * log(n0x / n) + n1x / n * log(n1x / n)), -1 *
(nx0 / n * log(nx0 / n) + nx1 / n * log(nx1 / n))),
maxconfidence = pmax(n11 / n1x, n11 / nx1),
jMeasure = n11 / n * log(n * n11 / (n1x * nx1)) +
n10 / n * log(n * n10 / (n1x * nx0)),
kulczynski = (n11 / n1x + n11 / nx1) / 2,
laplace = (n11 + 1) / (n1x + k),
certainty = (n11 / n1x - nx1 / n) / (1 - nx1 / n),
addedValue = n11 / n1x - nx1 / n,
ralambondrainy = n10 / n,
sebag = (n1x - n10) / n10,
counterexample = (n11 - n10) / n11,
# needs alpha
#if(measure == "wang") return(1/n * (1-alpha) * n1x - n10)
confirmedConfidence = (n11 - n10) / n1x,
casualSupport = (n1x + nx1 - 2 * n10) / n,
casualConfidence = 1 - n10 / n * (1 / n1x + 1 / nx1),
leastContradiction = (n1x - n10) / nx1,
centeredConfidence = nx0 / n - n10 / n1x,
varyingLiaison = (n1x - n10) / (n1x * nx1 / n) - 1,
yuleQ = {
OR <- n11 * n00 / (n10 * n01)
(OR - 1) / (OR + 1)
},
yuleY = {
OR <- n11 * n00 / (n10 * n01)
(sqrt(OR) - 1) / (sqrt(OR) + 1)
},
lerman = (n11 - n1x * nx1 / n) / sqrt(n1x * nx1 / n),
implicationIndex = (n10 - n1x * nx0 / n) / sqrt(n1x * nx0 / n),
doc = (n11 / n1x) - (n01 / n0x),
chiSquared = {
chi2 <- numeric(length(x))
for (i in seq_len(length(x))) {
fo <- matrix(c(n00[i], n01[i], n10[i], n11[i]), ncol = 2)
#fe <- tcrossprod(c(nx0[i], nx1[i]), ic(n0x[i], n1x[i])) / n
## check if approximation is ok
## we don't do this now
##if(any(fe < 5)) chi2[i] <- nA
##else
#chi2[i] <- sum((fo - fe) ^ 2 / fe)
# warning about approximation
suppressWarnings(chi2[i] <-
stats::chisq.test(fo, correct = FALSE)$statistic)
}
## the chi square test has 1 df for a 2x2 contingency table.
## The critical value at alpha=0.05 is:
## qchisq(0.05, df =1, lower.tail=FALSE)
## [1] 3.841459
if (!significance)
chi2
else
stats::pchisq(q = chi2,
df = 1,
lower.tail = !compliment)
}
))
if (is.null(m))
stop("Specified measure not implemented.")
m
}
## RLD see Kenett and Salini 2008
## RLD code contributed by Silvia Salini
.RLD <- function(x, ...) {
counts <- .getCounts(x, ...)
RLD <- with(counts, {
RLD <- numeric(length(x))
for (i in seq_len(length(x))) {
D <- (n11[i] * n00[i] - n10[i] * n01[i]) / n
if (D > 0)
if (n01[i] < n10[i])
RLD[i] <- D / (D + n01[i])
else
RLD[i] <- D / (D + n10[i])
else
if (n11[i] < n00[i])
RLD[i] <- D / (D - n11[i])
else
RLD[i] <- D / (D - n00[i])
}
RLD
})
RLD[!is.finite(RLD)] <- NA
RLD
}
## Standardising the Lift of an Association Rule
# by McNicholas, 2008, DOI: 10.1016/j.csda.2008.03.013
.stdLift <-
function(rules,
transactions = NULL,
correct = TRUE) {
measures <- interestMeasure(rules,
c("support", "confidence", "lift", "coverage", "rhsSupport"),
transactions = transactions)
n <- info(rules)$ntransactions
if (is.null(n)) {
if (is.null(transactions))
stop("rules do not contain info from transactions. transactions are needed.")
n <- length(transactions)
}
supp_A <- measures$coverage
supp_B <- measures$rhsSupport
# upper bound of lift
lambda <- pmax(supp_A + supp_B - 1, 1 / n) / (supp_A * supp_B)
# correct lambda for min. confidence and min. support
if (correct) {
c <- info(rules)$confidence
s <- info(rules)$support
if (!is.null(c) && !is.null(s))
lambda <-
pmax(lambda, 4 * s / (1 + s) ^ 2, s / (supp_A * supp_B), c / supp_B)
else
warning(
"minimum support or confidence not available in info(x). Using uncorrected stdLift instead."
)
}
# lower bound of lift
upsilon <- 1 / pmax(supp_A, supp_B)
stdLift <- (measures$lift - lambda) / (upsilon - lambda)
stdLift[is.nan(stdLift)] <- 1
stdLift
}
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