R/bic.mm.fsreg.R

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

bic.mm.fsreg <- function( target, dataset, wei = NULL, tol = 0, ncores = 1) {

  p <- dim(dataset)[2]  ## number of variables
  bico <- numeric( p )
  moda <- list()
  k <- 1   ## counter
  n <- length(target)  ## sample size
  tool <- NULL
  oiko <- NULL
  info <- matrix( 0, ncol = 2 )
  #check for NA values in the dataset and replace them with the variable median or the mode
  if ( any( is.na(dataset) ) ) {
    warning("The dataset contains missing values (NA) and they were replaced automatically by the variable (column) median (for numeric) or by the most frequent level (mode) if the variable is factor")
    if ( is.matrix(dataset) )  {
      dataset <- apply( dataset, 2, function(x){ x[which(is.na(x))] = median(x, na.rm = TRUE) ; return(x) } ) 
    } else {
       poia <- unique( which( is.na(dataset), arr.ind = TRUE )[, 2] )
      for( i in poia )  {
        xi <- dataset[, i]
        if ( is.numeric(xi) ) {                    
          xi[ which( is.na(xi) ) ] <- median(xi, na.rm = TRUE) 
        } else if ( is.factor( xi ) )     xi[ which( is.na(xi) ) ] <- levels(xi)[ which.max( as.vector( table(xi) ) )]
        dataset[, i] <- xi
      }
    }
  }
 
  dataset <- as.data.frame(dataset)
    durat <- proc.time()
    mi = MASS::rlm( target ~ 1, maxit = 2000, weights = wei )
    ini = BIC(mi)

      if (ncores <= 1) {
	  
        for (i in 1:p) {
         mi <- MASS::rlm( target ~ dataset[, i], maxit = 2000, method = "MM")
         bico[i] <- BIC(mi)
        }

     mat <- cbind(1:p, bico)

    } else {
        cl <- makePSOCKcluster(ncores)
        registerDoParallel(cl)
        bico <- numeric(p)
        mod <- foreach( i = 1:p, .combine = rbind, export = "rlm", .packages = "MASS" ) %dopar% {
          ww <- MASS::rlm( target ~ dataset[, i], maxit = 2000, method = "MM")
          bico[i] = BIC(ww)
        }
        stopCluster(cl)
      mat <- cbind(1:p, mod)

    }
    colnames(mat) <- c("variable", "BIC")
    rownames(mat) <- 1:p
    sel <- which.min( mat[, 2] )
    sela <- sel

    if ( ini - mat[sel, 2] > tol ) {

        mi <- MASS::rlm( target ~ dataset[, sel], maxit = 2000, method = "MM")
        tool[1] <- BIC(mi)
      moda[[ 1 ]] <- mi
      info[1, ] <- mat[sel, ]
      mat <- mat[-sel, , drop = FALSE]
    } else  {
      info <- info  
      sela <- NULL
    }
    ######
    ###     k equals 2
    ######

    if ( length(moda) > 0 ) {

      k <- 2
      pn <- p - k + 1
      mod <- list()

      if ( ncores <= 1 ) {
          for (i in 1:pn) {
            ma <- MASS::rlm( target ~., data = dataset[, c(sel, mat[i, 1]) ], maxit = 2000, method = "MM")
            mat[i, 2] <- BIC( ma )
          }

      } else {
          cl <- makePSOCKcluster(ncores)
          registerDoParallel(cl)
          bico <- numeric(pn)
          mod <- foreach( i = 1:pn, .combine = rbind, export = "rlm", .packages = "MASS" ) %dopar% {
            ww <- MASS::rlm( target ~., data = dataset[, c(sel, mat[i, 1])], maxit = 2000, method = "MM")
            bico[i] <- BIC( ww )
          }
          stopCluster(cl)

        mat[, 2] <- mod
      }

       ina <- which.min( mat[, 2] )
       sel <- mat[ina, 1]
       if ( tool[1] - mat[ina, 2] <= tol ) {
        info <- info
      } else {

        tool[2] <- mat[ina, 2]
        info <- rbind(info, mat[ina, ] )
        sela <- info[, 1]
        mat <- mat[-ina, , drop = FALSE]
      }

    }
   #########
   ####      k is greater than 2
   #########
    if ( nrow(info) > 1 ) {
      while ( k < n - 15 & tool[ k - 1 ] - tool[ k ] > tol & nrow(mat) > 0 ) {
        
        k <- k + 1
        pn <- p - k + 1

        if ( ncores <= 1 ) {
             for ( i in 1:pn ) {
               ma <- MASS::rlm( target ~., data = dataset[, c(sela, mat[i, 1]) ], maxit = 2000, method = "MM")
               mat[i, 2] <- BIC( ma )
             }
			 
        } else {
             cl <- makePSOCKcluster(ncores)
             registerDoParallel(cl)
             bico <- numeric(pn)
             mod <- foreach( i = 1:p, .combine = rbind, export = "rlm", .packages = "MASS" ) %dopar% {
               ww <- MASS::rlm( target ~., data = dataset[, c(sela, mat[i, 1]) ], maxit = 2000, method = "MM")
               bico[i] <- BIC( ww )
             }
             stopCluster(cl)
           mat[, 2] <- mod
        }
         ina <- which.min( mat[, 2] )
         sel <- mat[ina, 1]

         if ( tool[k - 1] - mat[ina, 2]  <= tol ) {
           info <- rbind( info,  c( -10, 1e300 ) )
           tool[k] <- Inf

         } else {
 
           tool[k] <- mat[ina, 2]
           info <- rbind(info, mat[ina, ] )
           sela <- info[, 1]
           mat <- mat[-ina, , drop = FALSE]
        }

      }
    }

    duration <- proc.time() - durat

    d <- length(sela)
    final <- NULL

    if ( d >= 1 ) {
      final <- MASS::rlm( target ~., data = dataset[, sela], maxit = 2000, method = "MM")
      info <- info[1:d, , drop = FALSE]
      if ( d == 1 )  info <- matrix(info, nrow = 1)
      colnames(info) <- c( "variables", "BIC" )
      rownames(info) <- info[, 1]
    }  
    list( runtime = duration, mat = t(mat), info = info, ci_test = "testIndMMReg", final = final)
}