R/grow-shrink.R
In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference
grow.shrink.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]] = gs.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)
}#GROW.SHRINK.OPTIMIZED
grow.shrink.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), gs.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)
}#GROW.SHRINK.CLUSTER
grow.shrink = function(x, whitelist, blacklist, test, alpha, B,
strict, debug = FALSE) {
nodes = names(x)
# 1. [Compute Markov Blankets]
mb = lapply(as.list(nodes), gs.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)
}#GROW.SHRINK
gs.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
# 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))
# known.good nodes are not to be checked for inclusion, and "mb" is used
# for the shrinking phase instead of "nodes", we can just remove them.
nodes = nodes[!(nodes %in% known.good)]
# and vice versa X \not\in MB(Y) <=> Y \not\in MB(X)
known.bad = names(backtracking[!backtracking])
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
# grow phase.
repeat {
# store the current size of the Markov Blanket.
mb.size = length(mb)
for (y in nodes) {
if (debug)
cat(" * checking node", y, "for inclusion.\n")
a = conditional.test(x, y, mb, data = data, test = test, B = B,
alpha = alpha)
if (a <= alpha) {
# add the node to the Markov blanket.
mb = c(mb, y)
# do not check the same node again.
nodes = nodes[nodes != y]
if (debug) {
cat(" > node", y, "included in the markov blanket ( p-value:", a, ").\n")
cat(" > markov blanket (", length(mb), "nodes ) now is '", mb, "'.\n")
cat(" > restarting grow loop.\n")
}#THEN
break
}#THEN
else if (debug) {
cat(" >", x, "indep.", y, "given '", mb, "' ( p-value:", a, ").\n")
}#THEN
}#FOR
# if the Markov blanket is unchanged exit the grow phase.
if (length(mb) == mb.size) break
}#REPEAT
# whitelisted nodes are neighbours, they cannot be removed from the
# markov blanket; on the other hand, known.good nodes from backtracking
# should be checked to remove false positives.
nodes = mb[!(mb %in% whitelisted)]
# shrinking phase.
repeat {
# store the current size of the Markov Blanket.
mb.size = length(mb)
for (y in nodes) {
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) {
# update the markov blanket.
mb = mb[mb != y]
# do not check the same node again.
nodes = nodes[nodes != y]
if (debug) {
cat(" > node", y, "removed from the markov blanket. ( p-value:", a, ")\n")
cat(" > conditioning subset: '", mb, "'\n")
cat(" > restarting shrink loop.\n")
}#THEN
break
}#THEN
else if (debug) {
cat(" > node", y, "remains in the markov blanket. ( p-value:", a, ")\n")
}#THEN
}#FOR
# if the Markov blanket is unchanged exit the grow phase.
if (length(mb) == mb.size) break
}#REPEAT
return(mb)
}#GS.MARKOV.BLANKET
vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.
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grow.shrink.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]] = gs.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)
}#GROW.SHRINK.OPTIMIZED
grow.shrink.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), gs.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)
}#GROW.SHRINK.CLUSTER
grow.shrink = function(x, whitelist, blacklist, test, alpha, B,
strict, debug = FALSE) {
nodes = names(x)
# 1. [Compute Markov Blankets]
mb = lapply(as.list(nodes), gs.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)
}#GROW.SHRINK
gs.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
# 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))
# known.good nodes are not to be checked for inclusion, and "mb" is used
# for the shrinking phase instead of "nodes", we can just remove them.
nodes = nodes[!(nodes %in% known.good)]
# and vice versa X \not\in MB(Y) <=> Y \not\in MB(X)
known.bad = names(backtracking[!backtracking])
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
# grow phase.
repeat {
# store the current size of the Markov Blanket.
mb.size = length(mb)
for (y in nodes) {
if (debug)
cat(" * checking node", y, "for inclusion.\n")
a = conditional.test(x, y, mb, data = data, test = test, B = B,
alpha = alpha)
if (a <= alpha) {
# add the node to the Markov blanket.
mb = c(mb, y)
# do not check the same node again.
nodes = nodes[nodes != y]
if (debug) {
cat(" > node", y, "included in the markov blanket ( p-value:", a, ").\n")
cat(" > markov blanket (", length(mb), "nodes ) now is '", mb, "'.\n")
cat(" > restarting grow loop.\n")
}#THEN
break
}#THEN
else if (debug) {
cat(" >", x, "indep.", y, "given '", mb, "' ( p-value:", a, ").\n")
}#THEN
}#FOR
# if the Markov blanket is unchanged exit the grow phase.
if (length(mb) == mb.size) break
}#REPEAT
# whitelisted nodes are neighbours, they cannot be removed from the
# markov blanket; on the other hand, known.good nodes from backtracking
# should be checked to remove false positives.
nodes = mb[!(mb %in% whitelisted)]
# shrinking phase.
repeat {
# store the current size of the Markov Blanket.
mb.size = length(mb)
for (y in nodes) {
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) {
# update the markov blanket.
mb = mb[mb != y]
# do not check the same node again.
nodes = nodes[nodes != y]
if (debug) {
cat(" > node", y, "removed from the markov blanket. ( p-value:", a, ")\n")
cat(" > conditioning subset: '", mb, "'\n")
cat(" > restarting shrink loop.\n")
}#THEN
break
}#THEN
else if (debug) {
cat(" > node", y, "remains in the markov blanket. ( p-value:", a, ")\n")
}#THEN
}#FOR
# if the Markov blanket is unchanged exit the grow phase.
if (length(mb) == mb.size) break
}#REPEAT
return(mb)
}#GS.MARKOV.BLANKET
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