R/InternalMMPC.timeclass.R

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

InternalMMPC.timeclass = function(target, dataset, max_k, threshold, test=NULL, ini, wei=NULL, hash=FALSE, 
                                  varsize, stat_hash, pvalue_hash)  {
  #univariate feature selection test
  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.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
  ################ 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)#
  #   selectedVars = selectedVarsOrder[1:numberofSelectedVars]
  # #queues correctness
  # all_queues = queues
  # queues = queues[which(selectedVars==1)]
  # queues <- lapply(1:length(queues) , function(i){queues[[i]] = unique(queues[[i]])})
  #adjusting the results
  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)
}