R/fast-iamb.R
In vspinu/bnlearn: Bayesian network structure learning, parameter learning and inference
fast.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]] = fast.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)
}#FAST.INCREMENTAL.ASSOCIATION.OPTIMIZED
fast.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), fast.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)
}#FAST.INCREMENTAL.ASSOCIATION.CLUSTER
fast.incremental.association = function(x, whitelist, blacklist, test,
alpha, B, strict, debug = FALSE) {
nodes = names(x)
# 1. [Compute Markov Blankets]
mb = lapply(as.list(nodes), fast.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)
}#FAST.INCREMENTAL.ASSOCIATION
fast.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
insufficient.data = FALSE
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
# 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])
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[!(nodes %in% mb)], "'.\n")
}#THEN
}#THEN
repeat {
# growing phase.
# reset the insufficient.data boolean flag.
insufficient.data = FALSE
# get an association measure for each of the available nodes.
association = sapply(nodes[!(nodes %in% mb)], 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
# sort the candidates in increasing p-value order.
association = sort(association[which(association <= alpha)])
for (node in names(association)) {
opc = obs.per.cell(x, node, mb, data)
if (!(test %in% asymptotic.tests) || (opc >= 5)) {
if (debug) {
if (test %in% available.continuous.tests) {
cat(" @", node, "included in the markov blanket (p-value:",
association[node], ").\n")
}#THEN
else {
cat(" @", node, "included in the markov blanket (p-value:",
association[node], ", obs/cell:", opc, ").\n")
}#ELSE
cat(" > markov blanket (", length(mb) + 1, " nodes ) now is '", c(mb, node), "'.\n")
}#THEN
# speculatively add the node if the asymptotic behaviour of the
# statistical tests is still good.
mb = c(mb, node)
}#THEN
else {
if (debug)
cat(" @ not enough observations per cell (", opc ,"), skipping.\n")
# do not add new nodes if that compromises the asymptotic behaviour
# of the statistical tests.
insufficient.data = TRUE
break
}#ELSE
}#FOR
# shrinking phase.
mb.old.length = length(mb)
# 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.
if (length(mb) > 1)
sapply(mb[!(mb %in% whitelisted)], del.node, x = x, test = test)
# if there are not enough observations and no new node has been included
# in the markov blanket, stop iterating.
if (insufficient.data && (mb.old.length == length(mb))) break
# do not touch the check the nodes in the markov blanket again.
nodes = nodes[!(nodes %in% mb)]
}#REPEAT
return(mb)
}#FAST.IA.MARKOV.BLANKET
vspinu/bnlearn documentation built on May 3, 2019, 7:08 p.m.
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fast.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]] = fast.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)
}#FAST.INCREMENTAL.ASSOCIATION.OPTIMIZED
fast.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), fast.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)
}#FAST.INCREMENTAL.ASSOCIATION.CLUSTER
fast.incremental.association = function(x, whitelist, blacklist, test,
alpha, B, strict, debug = FALSE) {
nodes = names(x)
# 1. [Compute Markov Blankets]
mb = lapply(as.list(nodes), fast.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)
}#FAST.INCREMENTAL.ASSOCIATION
fast.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
insufficient.data = FALSE
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
# 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])
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[!(nodes %in% mb)], "'.\n")
}#THEN
}#THEN
repeat {
# growing phase.
# reset the insufficient.data boolean flag.
insufficient.data = FALSE
# get an association measure for each of the available nodes.
association = sapply(nodes[!(nodes %in% mb)], 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
# sort the candidates in increasing p-value order.
association = sort(association[which(association <= alpha)])
for (node in names(association)) {
opc = obs.per.cell(x, node, mb, data)
if (!(test %in% asymptotic.tests) || (opc >= 5)) {
if (debug) {
if (test %in% available.continuous.tests) {
cat(" @", node, "included in the markov blanket (p-value:",
association[node], ").\n")
}#THEN
else {
cat(" @", node, "included in the markov blanket (p-value:",
association[node], ", obs/cell:", opc, ").\n")
}#ELSE
cat(" > markov blanket (", length(mb) + 1, " nodes ) now is '", c(mb, node), "'.\n")
}#THEN
# speculatively add the node if the asymptotic behaviour of the
# statistical tests is still good.
mb = c(mb, node)
}#THEN
else {
if (debug)
cat(" @ not enough observations per cell (", opc ,"), skipping.\n")
# do not add new nodes if that compromises the asymptotic behaviour
# of the statistical tests.
insufficient.data = TRUE
break
}#ELSE
}#FOR
# shrinking phase.
mb.old.length = length(mb)
# 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.
if (length(mb) > 1)
sapply(mb[!(mb %in% whitelisted)], del.node, x = x, test = test)
# if there are not enough observations and no new node has been included
# in the markov blanket, stop iterating.
if (insufficient.data && (mb.old.length == length(mb))) break
# do not touch the check the nodes in the markov blanket again.
nodes = nodes[!(nodes %in% mb)]
}#REPEAT
return(mb)
}#FAST.IA.MARKOV.BLANKET
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