329 lines
10 KiB
R
329 lines
10 KiB
R
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modules::import(openxlsx)
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modules::import(bnlearn)
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modules::import(stringr)
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modules::import(graph)
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modules::import(ggplot2)
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modules::import(stats)
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modules::import(plotly)
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modules::import(utils)
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#Improvements needed: make the selection of first row/column of nodes programmatic
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FIRST_NODE_COL <- 3
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mappings <- c('TestScenario', 'Map_P_BA', 'Map_BA_OP', 'Map_OP_ES')
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nodeTypes <- c('Input.Nodes', 'Internal.Nodes', 'Published.Nodes')
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states <- c('impact', 'confidence', 'growth', 'recovery')
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refs <-c(1:length(mappings))
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setEmpties <- function(val) {
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if (is.na(val)) return(0) else return(val)
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}
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readXL <- function(fName, sheetN, startRow=1) {
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xl <- read.xlsx(fName, sheet = sheetN, startRow) #, rowNames = import)
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return(data.frame(xl, stringsAsFactors = FALSE, row.names = NULL))
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}
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delNA <- function(vec) {
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return(vec[!is.na(vec)])
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}
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parseScenario <- function(press, prefix = 'p') {
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pressNames <- colnames(press)[2:length(colnames(press))]
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coefs <- matrix(data=NA, nrow=length(pressNames), ncol=2, dimnames=list(NULL, c('growth', 'confidence')))
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for (col in 2:ncol(press)) {
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coefs[col-1,] <- as.numeric(split(press[1, col]))[match(c('growth', 'confidence'), states)]
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}
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press[is.na(press)] <- 0
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if (sum(duplicated(pressNames))>0) {
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cat('Duplicated pressure node names found')
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print(pressNodes[duplicated(pressNames)])
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}
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return(list(
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timeSeq=press,
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nodes=data.frame(name = pressNames,
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code=paste0(prefix, seq(1:length(pressNames))),
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growth = coefs[,'growth'],
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confidence=coefs[,'confidence'],
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stringsAsFactors = FALSE),
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edges=data.frame(input=NULL, output=NULL, impact=NULL)
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))
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}
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getInitial <- function(string, letter) {
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return(tolower(substr(string, start=1, stop=1)))
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}
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split <- function(cell) {
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params <- unlist(strsplit(cell, ','))
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values <- rep(0, length(states))
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for (n in 1:length(params)) {
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kvp <- unlist(strsplit(params[n], '='))
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ref <- match(getInitial(trimws(kvp[1])), getInitial(states))
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if ((ref>0) & (ref<=length(values))) {
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values[ref] <- kvp[2]
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} else {
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print(paste('Unrecognised parameter(s):',params[n]))
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}
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}
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return(values)
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}
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cleanTitles <- function(titleV) {
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return(str_replace_all(titleV, c(' ' = '.', '-' = '')))
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}
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getOutNodes <- function(codes, codeList) {
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v <- vector(mode='logical', length=length(codes))
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for (idx in 1:length(codes)) {
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v[idx] <- (sum(startsWith(codes[idx], codeList))>0)
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}
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return(v)
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}
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buildGraph <- function(model, desc) {
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#model contains the following
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# node table, edge table
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#descriptor (desc) contains:
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#inputCode - the top layer of the model
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#outputCodes - all subsequent layers to be included in the model
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inputNodes <- model$nodes$code[which(startsWith(model$nodes$code, desc$inputCode))]
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inputText <- paste0("[", inputNodes, "]", collapse ="")
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#do the internal nodes
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edges <- ""
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outNodes <- model$nodes$code[getOutNodes(model$nodes$code, desc$outputCodes)]
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outDist <- vector(mode="list", length=length(outNodes))
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for (idx in 1:length(outNodes)) {
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nodeRef <- match(outNodes[idx], model$nodes$code)
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rows <- which(model$edges$output == outNodes[idx])
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inputsStr <- paste0(model$edges$input[which(model$edges$output == outNodes[idx])], sep=":", collapse="")
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edges <- paste0(edges, paste0("[", outNodes[idx], "|", substr(inputsStr, start=1, stop=(nchar(inputsStr)-1)), "]"))
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#Make the coefficient of the distribution
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coefVal <- setNames(c(model$nodes$growth[nodeRef], model$edges$impact[rows]),
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c("(Intercept)", model$edges$input[rows])
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)
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#str(coefVal)
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outDist[[idx]] <- list(coef = coefVal,
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sd = model$nodes$confidence[nodeRef])
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}
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print('about to build network')
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net <- model2network(paste0(inputText, edges))
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print('network build successful')
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inDist <- vector(mode="list", length=length(inputNodes))
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for (idx in 1:length(inputNodes)) {
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inRef <- match(inputNodes[idx], model$nodes$code)
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coefVal <- setNames(model$nodes$growth[inRef], "(Intercept)")
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inDist[[idx]] <- list(coef = coefVal, sd = model$nodes$confidence[inRef])
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}
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allDists = as.list(setNames(c(inDist, outDist), c(inputNodes, outNodes)))
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cfit = custom.fit(net, allDists)
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cat('about to calculate sample distributions')
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print(outNodes)
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sampleDists <- cpdist(cfit, nodes = outNodes, evidence = TRUE, n = 10000, method = "lw")
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summDists <- summary(sampleDists)
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#stdDev <- sd(sampleDists)
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print('sample distribution build successful')
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model$edges$input <- model$nodes$name[match(model$edges$input, model$nodes$code)]
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model$edges$output <- model$nodes$name[match(model$edges$output, model$nodes$code)]
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return(
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list(
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nodes = model$nodes,
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edges = model$edges,
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net = net,
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cfit = cfit,
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allDists = allDists,
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summDists = summDists
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)
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)
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}
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getValidNodes <- function(mapping, prevOutputs, prefix) {
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#Find row id for input nodes, internal and published
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inputNodes <- mapping[2:nrow(mapping),1]
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#check that all input nodes are in the previous table
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inputNodes <- delNA(mapping[mapping[,"Node.Type"] == 'input', "Nodes"])
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if (length(inputNodes)>0) {
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if (sum(inputNodes %in% prevOutputs$name)<length(inputNodes)) {
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cat('Missing entries for input nodes in previous output columns')
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print(inputNodes[!inputNodes %in% prevOutputs$name])
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}
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} else print('Invalid sheet - table must have at least one input row containing names from previous table')
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#Check the row headings concur with previous names
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validInputs <- delNA(inputNodes[which(unique(inputNodes) %in% prevOutputs$name)])
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if (length(validInputs)==0) print('Invalid sheet - table must have at least one input row containing names from previous table')
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inputInts <- delNA(inputNodes[mapping$Node.Type!='link'])
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if (sum(duplicated(inputInts))>0) {
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cat('Duplicated input node names found')
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print(inputNodes[duplicated(inputNodes)])
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}
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outNodes <- delNA(colnames(mapping)[FIRST_NODE_COL:ncol(mapping)])
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if (sum(duplicated(outNodes))>0) {
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cat('Duplicated output node names found')
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print(outNodes[duplicated(outNodes)])
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}
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#check that all internal nodes are in the columns
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intNodes <- delNA(mapping[mapping[,"Node.Type"] == 'internal', "Nodes"])
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if (length(intNodes)>0) {
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if (sum(intNodes %in% outNodes)<length(intNodes)) {
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cat('Missing entries for internal nodes in output columns')
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print(intNodes[!intNodes %in% outNodes])
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}
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}
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coefs <- matrix(data=NA, nrow=length(outNodes), ncol=2, dimnames=list(NULL, c('growth', 'confidence')))
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for (idx in 1:length(outNodes)) {
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col <- match(outNodes[idx], colnames(mapping))
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coefs[idx,] <- as.numeric(split(mapping[1, col]))[match(c('growth', 'confidence'), states)]
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}
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return(data.frame(
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code=c(prevOutputs$code, paste0(prefix, seq(1:length(outNodes)))),
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name=c(prevOutputs$name, outNodes),
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growth=c(prevOutputs$growth, coefs[,"growth"]),
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confidence=c(prevOutputs$confidence, coefs[,"confidence"]),
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stringsAsFactors=FALSE
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))
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}
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getCode <- function(name, nodeDF) {
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nodeDF$code[match(name, nodeDF$name)]
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}
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getValidEdges <- function(mapping, nodeDF, prevEdge=NULL, prefix) {
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str(nodeDF)
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edgeCols <- c('inputNode', 'outputNode', 'impact')
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edgeM <- matrix(data=NA, nrow=0, ncol=length(edgeCols), dimnames=list(NULL, edgeCols))
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#to start let just get the statements and print them out....
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for (col in FIRST_NODE_COL:ncol(mapping)) {
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count=0
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for (row in 2:nrow(mapping)) {
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if (!is.na(mapping[row, col])) {
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edgeM <- rbind(edgeM,
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c(getCode(mapping[row, 1], nodeDF),
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getCode(colnames(mapping)[col], nodeDF),
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split(mapping[row,col])[match('impact', states)]
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)
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)
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count=count+1
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}
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#if (count==0) print(paste('No edges found for output', colnames(mapping)[col]))
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}
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}
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if (is.null(prevEdge)) return (
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data.frame(
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input = edgeM[,"inputNode"],
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output = edgeM[,"outputNode"],
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impact = as.numeric(edgeM[,"impact"]),
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stringsAsFactors = FALSE
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)
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) else return (
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data.frame(
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input = c(prevEdge$input, edgeM[,"inputNode"]),
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output = c(prevEdge$output, edgeM[,"outputNode"]),
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impact = c(prevEdge$impact, as.numeric(edgeM[,"impact"])),
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stringsAsFactors = FALSE
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)
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)
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}
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parseMapping <- function(mapping, prevOutputs, prefix) {
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mapping <- mapping[,-1]
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mapping[,1] <- cleanTitles(mapping[,1])
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nodeDF <- getValidNodes(mapping, prevOutputs$nodes, prefix)
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edgeDF <- getValidEdges(mapping, nodeDF, prevEdge=prevOutputs$edges, prefix)
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return(list(
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#New structure
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nodes=nodeDF,
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edges=edgeDF
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))
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}
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parseSheet <- function(fName) {
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#get sheet names
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print(paste('starting sheet load', fName))
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if (file.exists(fName)) {
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names <- openxlsx::getSheetNames(fName)
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if (length(names)>0) {
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sheets <- sort(delNA(match(names, mappings)))
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cat('starting sheet parse')
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print(sheets)
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if (sum(sheets==refs)==length(refs)) {
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#read all mapping tables
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scenario <- parseScenario(readXL(fName,mappings[1], startRow=1), prefix='p')
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p_ba <- parseMapping(readXL(fName,mappings[2], startRow=1), scenario, prefix='ba')
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p_op <- parseMapping(readXL(fName,mappings[3], startRow=1), p_ba, prefix='op')
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p_es <- parseMapping(readXL(fName,mappings[4], startRow=1), p_op, prefix='es')
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print('building graphs')
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p_baNet <- buildGraph(p_ba, desc=list(inputCode='p', outputCodes='ba'))
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p_opNet <- buildGraph(p_op, desc=list(inputCode='p', outputCodes=c('ba', 'op')))
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p_esNet <- buildGraph(p_es, desc=list(inputCode='p', outputCodes=c('ba', 'op', 'es')))
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print('sheet load completed')
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return(
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list(
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pressBioAss = p_baNet,
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pressOpProc = p_opNet,
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pressEcoServ = p_esNet
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)
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)
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} else {
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print(paste('Sheets found include', mappings[sheets]))
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cat('Missing sheets are:')
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print(refs[-sheets])
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}
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}
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}
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}
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