Nothing
R/Sectorial.R
In TreeSearch: Phylogenetic Analysis with Discrete Character Data
########Sectorial <- function (tree, dataset, TreeScorer = TODO, sectRearrangements=list(RootedNNI),
######## searchRearrangements=list(RootedNNI, RootedTBR, RootedNNI),
######## maxHits=c(30, 40, 60), maxIter=2000, verbosity=3, ...) {
######## best.score <- attr(tree, "score")
######## if (is.null(treeOrder <- attr(tree, "order")) || treeOrder != "preorder") tree <- Preorder(tree)
########
######## tree <- RenumberTips(tree, names(dataset))
######## if (length(best.score) == 0) best.score <- TreeScorer(tree, dataset, ...)
######## sect <- DoSectorial(tree, dataset, verbosity=verbosity-1, maxit=30,
######## maxIter=max(maxIter), maxHits=15, smallestSector=6,
######## largestSector=dim(tree$edge)[1]*0.25, Rearrangements=sectRearrangements)
######## for (i in seq_along(sectRearrangements)) {
######## iters <- if (length(maxIter) <= i) maxIter[[i]] else min(maxIter)
######## hits <- if (length(maxHits) <= i) maxHits[[i]] else min(maxHits)
######## sect <- TreeSearch(sect, dataset, TreeScorer, sectRearrangements[[i]], maxIter=iters,
######## maxHits=hits, verbosity=verbosity-1)
######## }
######## if (attr(sect, "score") <= best.score) {
######## return (sect)
######## } else return (tree)
########}
########
#########' Sector Data
#########' Check that chosen sector contains parsimony-informative data
#########'
#########' The function simply checks that some characters have more than one state.
#########" It"s crude, but the cost of a false positive is low.
#########'
#########' @param dataset the dataset to subsample
#########' @param tips character vector listing tips that exist in the sector
#########'
#########' @keywords internal
#########' @export
########SectorHasData <- function (dataset, tips) {
######## if (class(dataset) =="phyDat") {
######## levs <- attr(dataset, "levels")
######## contrast <- attr(dataset, "contrast")
######## index <- as.integer(contrast %*% 2L ^ (seq_along(attr(dataset, "levels")) - 1))
######## characters <- vapply(dataset, function (X) index[X], integer(attr(dataset, "nr")))
######## } else if (names(dataset)) {
######## characters <- vapply(dataset, c, integer(length(dataset[[1]])))
######## } else if(rownames(dataset)) {
######## characters <- t(dataset)
######## } else characters <- dataset
######## tokens <- apply(characters[, tips], 2, function (x) length(unique(x)))
######## return (any(tokens > 1))
########}
########
#######' Sectorial Search with inapplicable data
#######'
#######' \code{DoSectorial} is called by Sectorial
#######'
#######' @template preorderTreeParam
#######' @param dataset a dataset in the format expected by \code{TreeScorer}
#######' @param TreeScorer a function that will score a tree topology
#######' @param maxSectIter maximum number of sectorial iterations to perform
#######' @param maxIter maximum number of iterations to perform in tree rearrangement functions
#######' @param maxImprovements maximum number of times to find an optimal score before ending sectorial search
#######' @param Rearrangements a list of tree rearrangement functions that retain the root of the tree
#######' (e.g. \code{list(RootedSPR)})
#######' @param smallestSector integer giving size of smallest sector to rearrange
#######' @param largestSector integer giving size of largest sector to rearrange (rounded down if non-integral)
#######' @template verbosityParam
#######' @template treeScorerDots
#######'
#######' @return a tree of class \code{phylo} with a \code{TreeScorer} score as good or better than that of \code{tree}
#######'
#######' @author Martin R. Smith
#######' @importFrom ape root
#######' @export
######MorphySectorial <- function (parent, child, dataset, TreeScorer = MorphyLength, maxSectIter=100,
###### maxIter=500, maxImprovements=5, smallestSector=4, largestSector=1e+06,
###### Rearrangements=list(RootedNNI), verbosity=0, ...) {
###### if (verbosity >= 0) message(" - Sectorial search: optimizing sectors of", smallestSector, "to", floor(largestSector), "tips")
###### nEdge <- length(parent)
###### nTip <- (nEdge / 2) + 1
###### nonRootNodes <- (nTip + 2):(nEdge + 1)
######
###### epsilon <- 1e-08
###### improvements <- 1
######
###### NodeChildren <- function (parent, child) {
###### result <- integer(nEdge + 1L)
###### result[1:nTip] <- 1
###### edgeCounted <- child <= nTip
###### while (any(!counted)) {
######
###### result[!counted] <- result[!counted] +
######
###### }
###### }
###### for (i in seq_len(maxSectIter)) {
######
###### nodeLengths <- CladeSizes(tree, nonRootNodes)
###### candidateNodes <- nonRootNodes[nodeLengths >= smallestSector & nodeLengths <= largestSector]
###### if (verbosity >= 0) message("\n - Iteration ", i, "- attempting sectorial search on node ")
###### repeat {
###### sector <- sample(candidateNodes, 1)
###### candidate <- Subtree(tree, sector)
###### crownTips <- candidate$tip.label
###### sectorSize <- length(crownTips)
###### message(sector, "size", sectorSize, "...")
######
###### if (SectorHasData(dataset, crownTips)) break else message("unsuitable (no dataset); trying")
######
###### candidateNodes <- candidateNodes[-match(sector, candidateNodes)]
###### if (length(candidateNodes == 0)) stop("No selectable sectors contain parsimony information! Either "largestSector" is close to "smallestSector" or your dataset is short of parsimony information.")
###### }
###### if (verbosity >= 0) message(" Sector OK.")
######
###### crown <- root(AddTip(crown, 0, "SECTOR_ROOT"), length(crown$tip.label) + 1, resolve.root=TRUE)
###### initialScore <- TreeScorer(candidate, dataset, ...)
###### attr(candidate, "score") <- initialScore
######
###### if (verbosity >= 0) message("\n - Rearranging sector", sector)
###### for (Rearrange in Rearrangements) {
###### candidate <- TreeSearch(candidate, dataset, TreeScorer, Rearrange,
###### verbosity=verbosity-1, maxIter=maxIter, ...)
###### }
###### candidateScore <- attr(candidate, "score")
######
###### if((candidateScore + epsilon) < initialScore) {
###### improvements <- improvements + 1
######
###### subtree.labels <- crownTips
###### subtree.nTips <- sectorSize
###### subtree.edge <- candidate$edge
###### subtree.parent <- subtree.edge[, 1]
###### subtree.child <- subtree.edge[, 2]
######
###### isTip <- subtree.child <= subtree.nTips
###### subtree.child[isTip] <- match(crownTips[subtree.child[isTip]], tipOrder)
###### nodeAdjust <- sector - (subtree.nTips + 1)
######
###### subtree.child[!isTip] <- subtree.child[!isTip] + nodeAdjust
###### edges <- which(tree$edge[, 2] == sector) + seq_along(subtree.parent)
###### tree$edge[edges, 1] <- subtree.parent + nodeAdjust
###### tree$edge[edges, 2] <- subtree.child
######
###### if (verbosity > 0) message(" : improved local pscore, updated tree")
###### } else if (verbosity > 0) message (" : no improvement to local pscore")
###### if (improvements == maxImprovements) break()
###### } # for
###### if (verbosity >= 0)
###### message ("\nCompleted sectorial rearrangements.\n")
###### attr(tree, "score") <- NULL
###### attr(tree, "hits") <- NULL
###### # Return:
###### tree
######} # DoSectorial
######
#######' Sectorial Search
#######'
#######' \code{SectorialSearch} performs a sectorial search on a tree.
#######'
#######' A sectorial search detaches a random part of the tree, performs rearrangments
#######' on this subtree, then reattaches it to the main tree (Goloboff, 1999).
#######' The improvement to local \var{score} hopefully (but not necessarily) improves
#######' the overall \var{score}.
#######'
#######' \code{SectorialSearch} then uses this tree as a starting point for a series
#######' of tree rearrangements, by default using NNI, SPR and TBR swappers.
#######' It returns the new tree, unless the starting tree had a better score,
#######' in which case the starting tree is returned.
#######'
#######''
#######' @template treeParam
#######' @param dataset a dataset in the format required by `TreeScorer()`.
#######' @template InitializeDataParam
#######' @template CleanUpDataParam
#######' @template treeScorerParam
#########' @param Bootstrapper Function to perform bootstrapped rearrangements of tree.
#########' First arguments will be an edgeList and a dataset, initialized using \code{InitializeData}
#########' Should return a rearranged edgeList.
#######' @template swappersParam
#######' @param SectorialSwapper Function such as \code{\link{RootedNNISwap}} to use
#######' to rearrange sector.
#######' @param sectIter stop sectorial search when this many rearrangements have been performed.
#######' @param sectHits stop sectorial search when this many rearrangements have
#######' found the same best score.
#######' @param searchIter maximum rearrangements for subsequent search on whole tree.
#######' @param searchHits maximum times to hit best score before terminating whole-tree search.
#######' @template verbosityParam
#######' @template treeScorerDots
#######'
#######' @return a rooted tree of class \code{phylo}.
#######'
#######' @references \insertRef{Goloboff1999}{TreeSearch}
#######'
#######' @author Martin R. Smith
#######'
#######' @seealso \code{\link{TreeSearch}}
#######' @seealso \code{\link{MorphyRatchet}}
#######'
#######' @examples
#######" data("Lobo", package="TreeTools')
#######' njtree <- TreeTools::NJTree(Lobo.phy)
#######'
#######' \dontrun{
#######' SectorialSearch(njtree, Lobo.phy, maxIter=20, EdgeSwapper=NNISwap,
#######' ratchIter=1, maxIter=50, largest.sector=7)} # Will be time-consuming }
#######'
#######' @keywords tree
#######' @export
######SectorialSearch <- function (tree, dataset,
###### InitializeData = PhyDat2Morphy,
###### CleanUpData = UnloadMorphy,
###### TreeScorer = MorphyLength,
###### Bootstrapper = MorphyBootstrap,
###### swappers = list(TBRSwap, SPRSwap, NNISwap),
###### SectorialSwapper = swappers[[length(swappers)]],
###### returnAll=FALSE, stopAtScore=NULL,
###### sectIter=400L, sectHits=20L,
###### searchIter=sectIter * 5L, searchHits=sectHits * 2L,
###### verbosity=1L, ...) {
###### epsilon <- 1e-08
###### hits <- 0L
###### # initialize tree and data
###### if (dim(tree$edge)[1] != 2 * tree$Nnode) stop("tree must be bifurcating; try rooting with ape::root")
###### tree <- RenumberTips(tree, names(dataset))
###### edgeList <- tree$edge
###### edgeList <- RenumberEdges(edgeList[, 1], edgeList[, 2])
######
###### initializedData <- InitializeData(dataset)
###### on.exit(initializedData <- CleanUpData(initializedData))
######
###### bestScore <- if (is.null(attr(tree, "score"))) {
###### TreeScorer(edgeList[[1]], edgeList[[2]], initializedData, ...)
###### } else {
###### attr(tree, "score")
###### }
######
###### if (verbosity > 0L) message("\n* Beginning Sectorial Search, with initial score", bestScore)
###### if (!is.null(stopAtScore) && bestScore < stopAtScore + epsilon) return(tree)
######
######
###### # Rearrange a sector of the tree:
###### sect <- MorphySectorial(edgeList[[1]], edgelist[[2]], morphyObj, verbosity=verbosity-1, ratchIter=30,
###### maxIter=maxIter, maxHits=15, smallest.sector=6,
###### largest.sector=length(edgeList[[1]]) * 0.25, rearrangements=rearrangements)
######
###### if (class(swappers) == "function") swappers <- list(swappers)
###### # Now use sectorially rearranged tree as starting point for conventional search
###### edgeList <- EdgeListSearch(edgeList, initializedData, TreeScorer=TreeScorer,
###### EdgeSwapper=swappers, maxIter = maxIter,
###### maxHits = maxHits, verbosity = verbosity - 1L)
######
###### if (edgeList[[3]] <= bestScore) {
###### sect$edge <- cbind(edgeList[[1]], edgeList[[2]])
###### attr(sect, "score") <- edgeList[[3]]
###### attr(sect, "hits") <- edgeList[[4]]
###### # Return:
###### sect
###### } else {
###### # Return:
###### tree
###### }
######}
######
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########Sectorial <- function (tree, dataset, TreeScorer = TODO, sectRearrangements=list(RootedNNI),
######## searchRearrangements=list(RootedNNI, RootedTBR, RootedNNI),
######## maxHits=c(30, 40, 60), maxIter=2000, verbosity=3, ...) {
######## best.score <- attr(tree, "score")
######## if (is.null(treeOrder <- attr(tree, "order")) || treeOrder != "preorder") tree <- Preorder(tree)
########
######## tree <- RenumberTips(tree, names(dataset))
######## if (length(best.score) == 0) best.score <- TreeScorer(tree, dataset, ...)
######## sect <- DoSectorial(tree, dataset, verbosity=verbosity-1, maxit=30,
######## maxIter=max(maxIter), maxHits=15, smallestSector=6,
######## largestSector=dim(tree$edge)[1]*0.25, Rearrangements=sectRearrangements)
######## for (i in seq_along(sectRearrangements)) {
######## iters <- if (length(maxIter) <= i) maxIter[[i]] else min(maxIter)
######## hits <- if (length(maxHits) <= i) maxHits[[i]] else min(maxHits)
######## sect <- TreeSearch(sect, dataset, TreeScorer, sectRearrangements[[i]], maxIter=iters,
######## maxHits=hits, verbosity=verbosity-1)
######## }
######## if (attr(sect, "score") <= best.score) {
######## return (sect)
######## } else return (tree)
########}
########
#########' Sector Data
#########' Check that chosen sector contains parsimony-informative data
#########'
#########' The function simply checks that some characters have more than one state.
#########" It"s crude, but the cost of a false positive is low.
#########'
#########' @param dataset the dataset to subsample
#########' @param tips character vector listing tips that exist in the sector
#########'
#########' @keywords internal
#########' @export
########SectorHasData <- function (dataset, tips) {
######## if (class(dataset) =="phyDat") {
######## levs <- attr(dataset, "levels")
######## contrast <- attr(dataset, "contrast")
######## index <- as.integer(contrast %*% 2L ^ (seq_along(attr(dataset, "levels")) - 1))
######## characters <- vapply(dataset, function (X) index[X], integer(attr(dataset, "nr")))
######## } else if (names(dataset)) {
######## characters <- vapply(dataset, c, integer(length(dataset[[1]])))
######## } else if(rownames(dataset)) {
######## characters <- t(dataset)
######## } else characters <- dataset
######## tokens <- apply(characters[, tips], 2, function (x) length(unique(x)))
######## return (any(tokens > 1))
########}
########
#######' Sectorial Search with inapplicable data
#######'
#######' \code{DoSectorial} is called by Sectorial
#######'
#######' @template preorderTreeParam
#######' @param dataset a dataset in the format expected by \code{TreeScorer}
#######' @param TreeScorer a function that will score a tree topology
#######' @param maxSectIter maximum number of sectorial iterations to perform
#######' @param maxIter maximum number of iterations to perform in tree rearrangement functions
#######' @param maxImprovements maximum number of times to find an optimal score before ending sectorial search
#######' @param Rearrangements a list of tree rearrangement functions that retain the root of the tree
#######' (e.g. \code{list(RootedSPR)})
#######' @param smallestSector integer giving size of smallest sector to rearrange
#######' @param largestSector integer giving size of largest sector to rearrange (rounded down if non-integral)
#######' @template verbosityParam
#######' @template treeScorerDots
#######'
#######' @return a tree of class \code{phylo} with a \code{TreeScorer} score as good or better than that of \code{tree}
#######'
#######' @author Martin R. Smith
#######' @importFrom ape root
#######' @export
######MorphySectorial <- function (parent, child, dataset, TreeScorer = MorphyLength, maxSectIter=100,
###### maxIter=500, maxImprovements=5, smallestSector=4, largestSector=1e+06,
###### Rearrangements=list(RootedNNI), verbosity=0, ...) {
###### if (verbosity >= 0) message(" - Sectorial search: optimizing sectors of", smallestSector, "to", floor(largestSector), "tips")
###### nEdge <- length(parent)
###### nTip <- (nEdge / 2) + 1
###### nonRootNodes <- (nTip + 2):(nEdge + 1)
######
###### epsilon <- 1e-08
###### improvements <- 1
######
###### NodeChildren <- function (parent, child) {
###### result <- integer(nEdge + 1L)
###### result[1:nTip] <- 1
###### edgeCounted <- child <= nTip
###### while (any(!counted)) {
######
###### result[!counted] <- result[!counted] +
######
###### }
###### }
###### for (i in seq_len(maxSectIter)) {
######
###### nodeLengths <- CladeSizes(tree, nonRootNodes)
###### candidateNodes <- nonRootNodes[nodeLengths >= smallestSector & nodeLengths <= largestSector]
###### if (verbosity >= 0) message("\n - Iteration ", i, "- attempting sectorial search on node ")
###### repeat {
###### sector <- sample(candidateNodes, 1)
###### candidate <- Subtree(tree, sector)
###### crownTips <- candidate$tip.label
###### sectorSize <- length(crownTips)
###### message(sector, "size", sectorSize, "...")
######
###### if (SectorHasData(dataset, crownTips)) break else message("unsuitable (no dataset); trying")
######
###### candidateNodes <- candidateNodes[-match(sector, candidateNodes)]
###### if (length(candidateNodes == 0)) stop("No selectable sectors contain parsimony information! Either "largestSector" is close to "smallestSector" or your dataset is short of parsimony information.")
###### }
###### if (verbosity >= 0) message(" Sector OK.")
######
###### crown <- root(AddTip(crown, 0, "SECTOR_ROOT"), length(crown$tip.label) + 1, resolve.root=TRUE)
###### initialScore <- TreeScorer(candidate, dataset, ...)
###### attr(candidate, "score") <- initialScore
######
###### if (verbosity >= 0) message("\n - Rearranging sector", sector)
###### for (Rearrange in Rearrangements) {
###### candidate <- TreeSearch(candidate, dataset, TreeScorer, Rearrange,
###### verbosity=verbosity-1, maxIter=maxIter, ...)
###### }
###### candidateScore <- attr(candidate, "score")
######
###### if((candidateScore + epsilon) < initialScore) {
###### improvements <- improvements + 1
######
###### subtree.labels <- crownTips
###### subtree.nTips <- sectorSize
###### subtree.edge <- candidate$edge
###### subtree.parent <- subtree.edge[, 1]
###### subtree.child <- subtree.edge[, 2]
######
###### isTip <- subtree.child <= subtree.nTips
###### subtree.child[isTip] <- match(crownTips[subtree.child[isTip]], tipOrder)
###### nodeAdjust <- sector - (subtree.nTips + 1)
######
###### subtree.child[!isTip] <- subtree.child[!isTip] + nodeAdjust
###### edges <- which(tree$edge[, 2] == sector) + seq_along(subtree.parent)
###### tree$edge[edges, 1] <- subtree.parent + nodeAdjust
###### tree$edge[edges, 2] <- subtree.child
######
###### if (verbosity > 0) message(" : improved local pscore, updated tree")
###### } else if (verbosity > 0) message (" : no improvement to local pscore")
###### if (improvements == maxImprovements) break()
###### } # for
###### if (verbosity >= 0)
###### message ("\nCompleted sectorial rearrangements.\n")
###### attr(tree, "score") <- NULL
###### attr(tree, "hits") <- NULL
###### # Return:
###### tree
######} # DoSectorial
######
#######' Sectorial Search
#######'
#######' \code{SectorialSearch} performs a sectorial search on a tree.
#######'
#######' A sectorial search detaches a random part of the tree, performs rearrangments
#######' on this subtree, then reattaches it to the main tree (Goloboff, 1999).
#######' The improvement to local \var{score} hopefully (but not necessarily) improves
#######' the overall \var{score}.
#######'
#######' \code{SectorialSearch} then uses this tree as a starting point for a series
#######' of tree rearrangements, by default using NNI, SPR and TBR swappers.
#######' It returns the new tree, unless the starting tree had a better score,
#######' in which case the starting tree is returned.
#######'
#######''
#######' @template treeParam
#######' @param dataset a dataset in the format required by `TreeScorer()`.
#######' @template InitializeDataParam
#######' @template CleanUpDataParam
#######' @template treeScorerParam
#########' @param Bootstrapper Function to perform bootstrapped rearrangements of tree.
#########' First arguments will be an edgeList and a dataset, initialized using \code{InitializeData}
#########' Should return a rearranged edgeList.
#######' @template swappersParam
#######' @param SectorialSwapper Function such as \code{\link{RootedNNISwap}} to use
#######' to rearrange sector.
#######' @param sectIter stop sectorial search when this many rearrangements have been performed.
#######' @param sectHits stop sectorial search when this many rearrangements have
#######' found the same best score.
#######' @param searchIter maximum rearrangements for subsequent search on whole tree.
#######' @param searchHits maximum times to hit best score before terminating whole-tree search.
#######' @template verbosityParam
#######' @template treeScorerDots
#######'
#######' @return a rooted tree of class \code{phylo}.
#######'
#######' @references \insertRef{Goloboff1999}{TreeSearch}
#######'
#######' @author Martin R. Smith
#######'
#######' @seealso \code{\link{TreeSearch}}
#######' @seealso \code{\link{MorphyRatchet}}
#######'
#######' @examples
#######" data("Lobo", package="TreeTools')
#######' njtree <- TreeTools::NJTree(Lobo.phy)
#######'
#######' \dontrun{
#######' SectorialSearch(njtree, Lobo.phy, maxIter=20, EdgeSwapper=NNISwap,
#######' ratchIter=1, maxIter=50, largest.sector=7)} # Will be time-consuming }
#######'
#######' @keywords tree
#######' @export
######SectorialSearch <- function (tree, dataset,
###### InitializeData = PhyDat2Morphy,
###### CleanUpData = UnloadMorphy,
###### TreeScorer = MorphyLength,
###### Bootstrapper = MorphyBootstrap,
###### swappers = list(TBRSwap, SPRSwap, NNISwap),
###### SectorialSwapper = swappers[[length(swappers)]],
###### returnAll=FALSE, stopAtScore=NULL,
###### sectIter=400L, sectHits=20L,
###### searchIter=sectIter * 5L, searchHits=sectHits * 2L,
###### verbosity=1L, ...) {
###### epsilon <- 1e-08
###### hits <- 0L
###### # initialize tree and data
###### if (dim(tree$edge)[1] != 2 * tree$Nnode) stop("tree must be bifurcating; try rooting with ape::root")
###### tree <- RenumberTips(tree, names(dataset))
###### edgeList <- tree$edge
###### edgeList <- RenumberEdges(edgeList[, 1], edgeList[, 2])
######
###### initializedData <- InitializeData(dataset)
###### on.exit(initializedData <- CleanUpData(initializedData))
######
###### bestScore <- if (is.null(attr(tree, "score"))) {
###### TreeScorer(edgeList[[1]], edgeList[[2]], initializedData, ...)
###### } else {
###### attr(tree, "score")
###### }
######
###### if (verbosity > 0L) message("\n* Beginning Sectorial Search, with initial score", bestScore)
###### if (!is.null(stopAtScore) && bestScore < stopAtScore + epsilon) return(tree)
######
######
###### # Rearrange a sector of the tree:
###### sect <- MorphySectorial(edgeList[[1]], edgelist[[2]], morphyObj, verbosity=verbosity-1, ratchIter=30,
###### maxIter=maxIter, maxHits=15, smallest.sector=6,
###### largest.sector=length(edgeList[[1]]) * 0.25, rearrangements=rearrangements)
######
###### if (class(swappers) == "function") swappers <- list(swappers)
###### # Now use sectorially rearranged tree as starting point for conventional search
###### edgeList <- EdgeListSearch(edgeList, initializedData, TreeScorer=TreeScorer,
###### EdgeSwapper=swappers, maxIter = maxIter,
###### maxHits = maxHits, verbosity = verbosity - 1L)
######
###### if (edgeList[[3]] <= bestScore) {
###### sect$edge <- cbind(edgeList[[1]], edgeList[[2]])
###### attr(sect, "score") <- edgeList[[3]]
###### attr(sect, "hits") <- edgeList[[4]]
###### # Return:
###### sect
###### } else {
###### # Return:
###### tree
###### }
######}
######
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