R/incremental-association.R
In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference
incremental.association.optimized = function(x, whitelist, blacklist, test,
alpha, B, strict, debug = FALSE) {
nodes = names(x)
mb2 = mb = list()
# 1. [Compute Markov Blankets]
for (node in nodes) {
backtracking = unlist(sapply(mb, function(x){ node %in% x }))
mb[[node]] = ia.markov.blanket(node, data = x, nodes = nodes,
alpha = alpha, B = B, whitelist = whitelist, blacklist = blacklist,
backtracking = backtracking, test = test, debug = debug)
}#FOR
# check markov blankets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, mb = TRUE, debug = debug)
# 2. [Compute Graph Structure]
for (node in nodes) {
backtracking = unlist(sapply(mb2, function(x){ node %in% x[["nbr"]] }))
# save results in a copy of mb;
mb2[[node]] = neighbour(node, mb = mb, data = x, alpha = alpha,
B = B, whitelist = whitelist, blacklist = blacklist,
backtracking = backtracking, test = test, debug = debug)
}#FOR
# update mb with the results of neighbour().
mb = mb2
# check neighbourhood sets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, debug = debug)
return(mb)
}#INCREMENTAL.ASSOCIATION.OPTIMIZED
incremental.association.cluster = function(x, cluster, whitelist, blacklist,
test, alpha, B, strict, debug = FALSE) {
nodes = names(x)
# 1. [Compute Markov Blankets]
mb = parLapply(cluster, as.list(nodes), ia.markov.blanket, data = x,
nodes = nodes, alpha = alpha, B = B, whitelist = whitelist,
blacklist = blacklist, test = test, debug = debug)
names(mb) = nodes
# check markov blankets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, mb = TRUE, debug = debug)
# 2. [Compute Graph Structure]
mb = parLapply(cluster, as.list(nodes), neighbour, mb = mb, data = x,
alpha = alpha, B = B, whitelist = whitelist, blacklist = blacklist,
test = test, debug = debug)
names(mb) = nodes
# check neighbourhood sets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, debug = debug)
return(mb)
}#INCREMENTAL.ASSOCIATION.CLUSTER
incremental.association = function(x, whitelist, blacklist, test, alpha,
B, strict, debug = FALSE) {
nodes = names(x)
# 1. [Compute Markov Blankets]
mb = lapply(as.list(nodes), ia.markov.blanket, data = x, nodes = nodes,
alpha = alpha, B = B, whitelist = whitelist, blacklist = blacklist,
test = test, debug = debug)
names(mb) = nodes
# check markov blankets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, mb = TRUE, debug = debug)
# 2. [Compute Graph Structure]
mb = lapply(as.list(nodes), neighbour, mb = mb, data = x, alpha = alpha,
B = B, whitelist = whitelist, blacklist = blacklist, test = test,
debug = debug)
names(mb) = nodes
# check neighbourhood sets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, debug = debug)
return(mb)
}#INCREMENTAL.ASSOCIATION
ia.markov.blanket = function(x, data, nodes, alpha, B, whitelist, blacklist,
start = character(0), backtracking = NULL, test, debug = FALSE) {
nodes = nodes[nodes != x]
known.good = known.bad = c()
whitelisted = nodes[sapply(nodes,
function(y) { is.whitelisted(whitelist, c(x, y), either = TRUE) })]
mb = start
to.add = ""
# growing phase
if (debug) {
cat("----------------------------------------------------------------\n")
cat("* learning the markov blanket of", x, ".\n")
if (length(start) > 0)
cat("* initial set includes '", mb, "'.\n")
}#THEN
# whitelisted nodes are included by default (if there's a direct arc
# between them of course they are in each other's markov blanket).
# arc direction is irrelevant here.
mb = unique(c(mb, whitelisted))
nodes = nodes[!(nodes %in% mb)]
# blacklist is not checked, not all nodes in a markov blanket must be
# neighbours.
# use backtracking for a further screening of the nodes to be checked.
if (!is.null(backtracking)) {
# nodes whose markov blanket includes this node are included, because
# X \in MB(Y) <=> Y \in MB(X); they can be removed in the backward phase
# later on if they are false positives.
known.good = names(backtracking[backtracking])
mb = unique(c(mb, known.good))
# and vice versa X \not\in MB(Y) <=> Y \not\in MB(X)
known.bad = names(backtracking[!backtracking])
# known.good nodes are not to be checked for inclusion, and the "nodes"
# is resetted below so we can just remove them.
nodes = nodes[!(nodes %in% known.good)]
if (debug) {
cat(" * known good (backtracking): '", known.good, "'.\n")
cat(" * known bad (backtracking): '", known.bad, "'.\n")
cat(" * nodes still to be tested for inclusion: '", nodes, "'.\n")
}#THEN
}#THEN
# phase I (stepwise forward selection)
repeat {
# get an association measure for each of the available nodes.
association = sapply(nodes, conditional.test, x, sx = mb, test = test,
data = data, B = B, alpha = alpha)
if (debug) {
cat(" * checking nodes for association.\n")
sapply(names(association),
function(x) { cat(" >", x, "has p-value", association[x], ".\n")})
}#THEN
# stop if there are no candidates for inclusion.
if (all(association > alpha) || length(nodes) == 0 || is.null(nodes)) break
# get the one which maximizes the association measure.
to.add = names(which.min(association))
if (debug) {
cat(" @", to.add, "included in the markov blanket ( p-value:",
association[to.add], ").\n")
cat(" > markov blanket (", length(mb) + 1, "nodes ) now is '", c(mb, to.add), "'.\n")
}#THEN
if (association[to.add] <= alpha) {
mb = c(mb, to.add)
nodes = nodes[nodes != to.add]
}#THEN
}#REPEAT
# phase II (backward selection)
del.node = function(y, x, test) {
if (debug)
cat(" * checking node", y, "for exclusion (shrinking phase).\n")
a = conditional.test(x, y, mb[mb != y], data = data, test = test, B = B,
alpha = alpha)
if (a > alpha) {
if (debug) {
cat(" > node", y, "removed from the markov blanket. ( p-value:", a, ")\n")
cat(" > conditioning subset: '", mb[mb != y], "'\n")
}#THEN
# update the markov blanket.
assign("mb", mb[mb != y], envir = sys.frame(-3))
return(NULL)
}#THEN
else if (debug) {
cat(" > node", y, "remains in the markov blanket. ( p-value:", a, ")\n")
}#THEN
}#DEL.NODE
# whitelisted nodes are neighbours, they cannot be removed from the markov
# blanket; the last node added in phase I will never be removed, because
# the tests for inclusion and removal are identical; on the other hand,
# known.good nodes should be checked to remove false positives.
if (length(mb) > 1)
sapply(mb[!(mb %in% c(to.add, whitelisted))], del.node, x = x, test = test)
return(mb)
}#IA.MARKOV.BLANKET
vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.
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incremental.association.optimized = function(x, whitelist, blacklist, test,
alpha, B, strict, debug = FALSE) {
nodes = names(x)
mb2 = mb = list()
# 1. [Compute Markov Blankets]
for (node in nodes) {
backtracking = unlist(sapply(mb, function(x){ node %in% x }))
mb[[node]] = ia.markov.blanket(node, data = x, nodes = nodes,
alpha = alpha, B = B, whitelist = whitelist, blacklist = blacklist,
backtracking = backtracking, test = test, debug = debug)
}#FOR
# check markov blankets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, mb = TRUE, debug = debug)
# 2. [Compute Graph Structure]
for (node in nodes) {
backtracking = unlist(sapply(mb2, function(x){ node %in% x[["nbr"]] }))
# save results in a copy of mb;
mb2[[node]] = neighbour(node, mb = mb, data = x, alpha = alpha,
B = B, whitelist = whitelist, blacklist = blacklist,
backtracking = backtracking, test = test, debug = debug)
}#FOR
# update mb with the results of neighbour().
mb = mb2
# check neighbourhood sets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, debug = debug)
return(mb)
}#INCREMENTAL.ASSOCIATION.OPTIMIZED
incremental.association.cluster = function(x, cluster, whitelist, blacklist,
test, alpha, B, strict, debug = FALSE) {
nodes = names(x)
# 1. [Compute Markov Blankets]
mb = parLapply(cluster, as.list(nodes), ia.markov.blanket, data = x,
nodes = nodes, alpha = alpha, B = B, whitelist = whitelist,
blacklist = blacklist, test = test, debug = debug)
names(mb) = nodes
# check markov blankets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, mb = TRUE, debug = debug)
# 2. [Compute Graph Structure]
mb = parLapply(cluster, as.list(nodes), neighbour, mb = mb, data = x,
alpha = alpha, B = B, whitelist = whitelist, blacklist = blacklist,
test = test, debug = debug)
names(mb) = nodes
# check neighbourhood sets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, debug = debug)
return(mb)
}#INCREMENTAL.ASSOCIATION.CLUSTER
incremental.association = function(x, whitelist, blacklist, test, alpha,
B, strict, debug = FALSE) {
nodes = names(x)
# 1. [Compute Markov Blankets]
mb = lapply(as.list(nodes), ia.markov.blanket, data = x, nodes = nodes,
alpha = alpha, B = B, whitelist = whitelist, blacklist = blacklist,
test = test, debug = debug)
names(mb) = nodes
# check markov blankets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, mb = TRUE, debug = debug)
# 2. [Compute Graph Structure]
mb = lapply(as.list(nodes), neighbour, mb = mb, data = x, alpha = alpha,
B = B, whitelist = whitelist, blacklist = blacklist, test = test,
debug = debug)
names(mb) = nodes
# check neighbourhood sets for consistency.
mb = bn.recovery(mb, nodes = nodes, strict = strict, debug = debug)
return(mb)
}#INCREMENTAL.ASSOCIATION
ia.markov.blanket = function(x, data, nodes, alpha, B, whitelist, blacklist,
start = character(0), backtracking = NULL, test, debug = FALSE) {
nodes = nodes[nodes != x]
known.good = known.bad = c()
whitelisted = nodes[sapply(nodes,
function(y) { is.whitelisted(whitelist, c(x, y), either = TRUE) })]
mb = start
to.add = ""
# growing phase
if (debug) {
cat("----------------------------------------------------------------\n")
cat("* learning the markov blanket of", x, ".\n")
if (length(start) > 0)
cat("* initial set includes '", mb, "'.\n")
}#THEN
# whitelisted nodes are included by default (if there's a direct arc
# between them of course they are in each other's markov blanket).
# arc direction is irrelevant here.
mb = unique(c(mb, whitelisted))
nodes = nodes[!(nodes %in% mb)]
# blacklist is not checked, not all nodes in a markov blanket must be
# neighbours.
# use backtracking for a further screening of the nodes to be checked.
if (!is.null(backtracking)) {
# nodes whose markov blanket includes this node are included, because
# X \in MB(Y) <=> Y \in MB(X); they can be removed in the backward phase
# later on if they are false positives.
known.good = names(backtracking[backtracking])
mb = unique(c(mb, known.good))
# and vice versa X \not\in MB(Y) <=> Y \not\in MB(X)
known.bad = names(backtracking[!backtracking])
# known.good nodes are not to be checked for inclusion, and the "nodes"
# is resetted below so we can just remove them.
nodes = nodes[!(nodes %in% known.good)]
if (debug) {
cat(" * known good (backtracking): '", known.good, "'.\n")
cat(" * known bad (backtracking): '", known.bad, "'.\n")
cat(" * nodes still to be tested for inclusion: '", nodes, "'.\n")
}#THEN
}#THEN
# phase I (stepwise forward selection)
repeat {
# get an association measure for each of the available nodes.
association = sapply(nodes, conditional.test, x, sx = mb, test = test,
data = data, B = B, alpha = alpha)
if (debug) {
cat(" * checking nodes for association.\n")
sapply(names(association),
function(x) { cat(" >", x, "has p-value", association[x], ".\n")})
}#THEN
# stop if there are no candidates for inclusion.
if (all(association > alpha) || length(nodes) == 0 || is.null(nodes)) break
# get the one which maximizes the association measure.
to.add = names(which.min(association))
if (debug) {
cat(" @", to.add, "included in the markov blanket ( p-value:",
association[to.add], ").\n")
cat(" > markov blanket (", length(mb) + 1, "nodes ) now is '", c(mb, to.add), "'.\n")
}#THEN
if (association[to.add] <= alpha) {
mb = c(mb, to.add)
nodes = nodes[nodes != to.add]
}#THEN
}#REPEAT
# phase II (backward selection)
del.node = function(y, x, test) {
if (debug)
cat(" * checking node", y, "for exclusion (shrinking phase).\n")
a = conditional.test(x, y, mb[mb != y], data = data, test = test, B = B,
alpha = alpha)
if (a > alpha) {
if (debug) {
cat(" > node", y, "removed from the markov blanket. ( p-value:", a, ")\n")
cat(" > conditioning subset: '", mb[mb != y], "'\n")
}#THEN
# update the markov blanket.
assign("mb", mb[mb != y], envir = sys.frame(-3))
return(NULL)
}#THEN
else if (debug) {
cat(" > node", y, "remains in the markov blanket. ( p-value:", a, ")\n")
}#THEN
}#DEL.NODE
# whitelisted nodes are neighbours, they cannot be removed from the markov
# blanket; the last node added in phase I will never be removed, because
# the tests for inclusion and removal are identical; on the other hand,
# known.good nodes should be checked to remove false positives.
if (length(mb) > 1)
sapply(mb[!(mb %in% c(to.add, whitelisted))], del.node, x = x, test = test)
return(mb)
}#IA.MARKOV.BLANKET
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