R/wald.Internalmmpc.R

In MXM: Feature Selection (Including Multiple Solutions) and Bayesian Networks

wald.Internalmmpc = function(target, dataset, max_k, threshold, test=NULL, ini=NULL, wei=NULL, user_test=NULL, hash=FALSE, varsize, 
                             stat_hash, pvalue_hash, targetID, ncores) {
  #univariate feature selection test
  if ( is.null(ini) ) { 
    univariateModels = wald.univregs(target = target, dataset = dataset, targetID = targetID, test = test, user_test = user_test, wei = wei, ncores = ncores) 
  } else  univariateModels = ini
  
  pvalues = univariateModels$pvalue;      
  stats = univariateModels$stat;
  #if we dont have any associations , return
  if ( min(pvalues, na.rm = TRUE) > threshold )  {
    #cat('No associations!');
    results = NULL;
    results$selectedVars = c();
    class(results$selectedVars) = "numeric";
    results$selectedVarsOrder = c();
    class(results$selectedVarsOrder) = "numeric";
    results$hashObject = NULL;
    class(results$hashObject) = 'list';
    class(results$univ) = 'list';
    results$pvalues = pvalues;
    results$stats = stats;
    results$univ = univariateModels
    results$max_k = max_k;
    results$threshold = threshold;
    results$n.tests <- length(stats)
    
    return(results);
  }
  #Initialize the data structs
  selectedVars = numeric(varsize);
  selectedVarsOrder = numeric(varsize);
  #select the variable with the highest association
  selectedVar = which( pvalues == pvalues[[which.min(pvalues)]] );
  selectedVars[selectedVar] = 1;
  selectedVarsOrder[selectedVar] = 1; #CHANGE
  #remaining variables to be considered
  remainingVars = numeric(varsize) + 1;
  remainingVars[selectedVar] = 0;
  remainingVars[pvalues > threshold] = 0;
  if (targetID > 0)  remainingVars[targetID] = 0;
  ################ main MMPC loop ################
  #main MMPC loop
  #loop until there are not remaining vars
  loop = any(as.logical(remainingVars));
  #rep = 1;
  while (loop) {
    max_min_results = max_min_assoc(target, dataset, test, wei, threshold, max_k, selectedVars, pvalues, stats, remainingVars , univariateModels, selectedVarsOrder, hash=hash, stat_hash=stat_hash, pvalue_hash=pvalue_hash);
    selectedVar = max_min_results$selected_var;
    selectedPvalue = max_min_results$selected_pvalue;
    remainingVars = max_min_results$remainingVars;
    pvalues = max_min_results$pvalues;
    stats = max_min_results$stats;
    stat_hash=max_min_results$stat_hash;
    pvalue_hash=max_min_results$pvalue_hash;
    #if the selected variable is associated with target , add it to the selected variables
    if ( selectedPvalue <= threshold ) {
      #print(paste("rep: ",rep,", selected var: ",selectedVar,", pvalue = ",exp(selectedPvalue)))
      #rep = rep + 1;
      selectedVars[selectedVar] = 1;
      selectedVarsOrder[selectedVar] = max(selectedVarsOrder) + 1;
      remainingVars[selectedVar] = 0;
    }
    
    loop = any(as.logical(remainingVars));
  }
  
  selectedVarsOrder[which(!selectedVars)] = varsize;#
  numberofSelectedVars = sum(selectedVars);#
  selectedVarsOrder = sort(selectedVarsOrder);#
  # queues <- lapply(1:length(queues) , function(i){queues[[i]] = unique(queues[[i]]);});
  #adjusting the results
  if(targetID > 0) {
    toAdjust <- which(selectedVars > targetID)
    selectedVars[toAdjust] = selectedVars[toAdjust] + 1
  }
  results = NULL;
  results$selectedVars = which( selectedVars == 1 )
  svorder = sort(pvalues[results$selectedVars], index.return = TRUE)
  svorder = results$selectedVars[svorder$ix]
  results$selectedVarsOrder = svorder
  hashObject = NULL
  hashObject$stat_hash = stat_hash
  hashObject$pvalue_hash = pvalue_hash
  results$hashObject = hashObject
  class(results$hashObject) = 'list'
  results$pvalues = pvalues
  results$stats = stats;
  results$univ = univariateModels
  results$max_k = max_k
  results$threshold = threshold
  results$n.tests <- length(stats) + length( hashObject$stat_hash )
  return(results)
}