R/simule.R

In JointNets: End-to-End Sparse Gaussian Graphical Model Simulation, Estimation, Visualization, Evaluation and Application

##A simplex solver for linear programming problem in (N)SIMULE
simule.linprogSPar <- function(i, Sigma, lambda)
  {
    # num of p * N
    # pTimesN = nrow(Sigma)
    # num of p * (N + 1)
    # Get parameters
    q = ncol(Sigma)
    p = ncol(Sigma) - nrow(Sigma)
    N = nrow(Sigma) / p
    # Generate e_j
    e = rep(0, p * N)
    for(j in 1:N){
        e[i + (j - 1) * p] = 1
    }
    # linear programming solution
    f.obj = rep(1, 2 * q)
    con1 = cbind(-Sigma, +Sigma)
    b1 = lambda - e
    b2 =  lambda + e
    f.con = rbind(-diag(2 * q), con1, -con1)
    f.rhs = c(rep(0, 2 * q), b1, b2)
    f.dir = rep("<=", length(f.rhs))
    lp.out = lp("min", f.obj, f.con, f.dir, f.rhs)
    beta = lp.out$solution[1:q] - lp.out$solution[(q + 1):(2 * q)]
    if (lp.out$status == 2) warning("No feasible solution!  Try a larger tuning parameter!")
    return(beta)
}

##' A constrained l1 minimization approach for estimating multiple Sparse
##' Gaussian or Nonparanormal Graphical Models
##' Estimate multiple, related sparse Gaussian or Nonparanormal graphical
##'
##' models from multiple related datasets using the SIMULE algorithm. Please
##' run demo(simule) to learn the basic functions provided by this package.
##' For further details, please read the original paper: Beilun Wang,
##' Ritambhara Singh, Yanjun Qi (2017) \doi{10.1007/s10994-017-5635-7}.
##'
##' The SIMULE algorithm is a constrained l1 minimization method that can
##' detect both the shared and the task-specific parts of multiple graphs
##' explicitly from data (through jointly estimating multiple sparse Gaussian
##' graphical models or Nonparanormal graphical models). It solves the
##' following equation: \deqn{ \hat{\Omega}^{(1)}_I, \hat{\Omega}^{(2)}_I,
##' \dots, \hat{\Omega}^{(K)}_I, \hat{\Omega}_S =
##' \min\limits_{\Omega^{(i)}_I,\Omega_S}\sum\limits_i ||\Omega^{(i)}_I||_1+
##' \epsilon K||\Omega_S||_1 } Subject to : \deqn{
##' ||\Sigma^{(i)}(\Omega^{(i)}_I + \Omega_S) - I||_{\infty} \le \lambda_{n}, i
##' = 1,\dots,K \nonumber } Please also see the equation (7) in our paper. The
##' \eqn{\lambda_n} is the hyperparameter controlling the sparsity level of the
##' matrices and it is the \code{lambda} in our function. The \eqn{\epsilon} is
##' the hyperparameter controlling the differences between the shared pattern
##' among graphs and the individual part of each graph. It is the
##' \code{epsilon} parameter in our function and the default value is 1. For
##' further details, please see our paper:
##' <http://link.springer.com/article/10.1007/s10994-017-5635-7>.
##'
##' @param X A List of input matrices. They can be data matrices or
##' covariance/correlation matrices. If every matrix in the X is a symmetric
##' matrix, the matrices are assumed to be covariance/correlation matrices.
##' @param lambda A positive number. The hyperparameter controls the sparsity
##' level of the matrices. The \eqn{\lambda_n} in the following section:
##' Details.
##' @param epsilon A positive number. The hyperparameter controls the
##' differences between the shared pattern among graphs and the individual part
##' of each graph. The \eqn{\epsilon} in the following section: Details. If
##' epsilon becomes larger, the generated graphs will be more similar to each
##' other. The default value is 1, which means that we set the same weights to
##' the shared pattern among graphs and the individual part of each graph.
##' @param covType A parameter to decide which Graphical model we choose to
##' estimate from the input data.
##'
##' If covType = "cov", it means that we estimate multiple sparse Gaussian
##' Graphical models. This option assumes that we calculate (when input X
##' represents data directly) or use (when X elements are symmetric
##' representing covariance matrices) the sample covariance matrices as input
##' to the simule algorithm.
##'
##' If covType = "kendall", it means that we estimate multiple nonparanormal
##' Graphical models. This option assumes that we calculate (when input X
##' represents data directly) or use (when X elements are symmetric
##' representing correlation matrices) the kendall's tau correlation matrices
##' as input to the simule algorithm.
##' @param intertwined indicate whether to use intertwined covariance matrix
##' @param parallel A boolean. This parameter decides if the package will use
##' the multithreading architecture or not.
##' @return \item{$graphs}{A list of the estimated inverse
##' covariance/correlation matrices.} \item{$share}{The shared graph among
##' multiple tasks.}
##' @author Beilun Wang
##' @references Beilun Wang, Ritambhara Singh, Yanjun Qi (2017).  A constrained
##' L1 minimization approach for estimating multiple Sparse Gaussian or
##' Nonparanormal Graphical Models.
##' http://link.springer.com/article/10.1007/s10994-017-5635-7
##' @export
##' @import lpSolve
##' @import parallel
##' @import pcaPP
##' @details if labels are provided in the datalist as column names, result will contain labels (to be plotted)
##' @examples
##' library(JointNets)
##' data(exampleData)
##' result = simule(X = exampleData , lambda = 0.1, epsilon = 0.45, covType = "cov", FALSE)
##' plot(result)
simule <- function(X, lambda, epsilon = 1, covType = "cov", intertwined = FALSE, parallel = FALSE)
  {

    N = length(X)
    for (i in 1:N){
    X[[i]] = compute_cov(X[[i]],covType)
    }

    if (intertwined){
      X = intertwined(X,covType = covType)
    }

    # initialize the parameters
    Graphs = list()
    p = ncol(X[[1]])
    xt = matrix(0, (N + 1) * p, p)
    I = diag(1, p, p)
    Z = matrix(0, p, p)
    # generate the condition matrix A
    A = X[[1]]
    for(i in 2:N){
        A = cbind(A,Z)
    }
    A = cbind(A,(1/(epsilon * N))*X[[1]])
    for(i in 2:N){
        temp = Z
        for(j in 2:N){
            if (j == i){
                temp = cbind(temp,X[[i]])
            }
            else{
                temp = cbind(temp,Z)
            }
        }
        temp = cbind(temp, 1/(epsilon * N) * X[[i]])
        A = rbind(A, temp)
    }
    # define the function f for parallelization
    f = function(x) simule.linprogSPar(x, A, lambda)

    if(parallel == TRUE){ # parallel version
    	# number of cores to collect,
    	# default number is number cores in your machine - 1,
    	# you can set your own number by changing this line.
        no_cores = detectCores() - 1
        cl = makeCluster(no_cores)
        # declare variable and function names to the cluster
        clusterExport(cl, list("f", "A", "lambda", "simule.linprogSPar", "lp"), envir = environment())
        result = parLapply(cl, 1:p, f)
        #print('Done!')
        for (i in 1:p){
            xt[,i] = result[[i]]
        }
        stopCluster(cl)
    }else{ # single machine code
        for (i in 1 : p){
            xt[,i] = f(i)
            if (i %% 10 == 0){
                cat("=")
                if(i %% 100 == 0){
                    cat("+")
                }
            }
        }
        #print("Done!")
    }

    for(i in 1:N){
    	# combine the results from each column. (\hat{\Omega}_{tot}^1)
        Graphs[[i]] = xt[(1 + (i-1) * p):(i * p),] + 1/(epsilon * N) * xt[(1 + N * p):((N + 1) * p),]
        # make it be symmetric
        for(j in 1:p){
            for(k in j:p){
                if (abs(Graphs[[i]][j,k]) < abs(Graphs[[i]][k,j])){
                    Graphs[[i]][j,k] = Graphs[[i]][j,k]
                    Graphs[[i]][k,j] = Graphs[[i]][j,k]
                }
                else{
                    Graphs[[i]][j,k] = Graphs[[i]][k,j]
                    Graphs[[i]][k,j] = Graphs[[i]][k,j]
                }
            }
        }
    }

    share = 1/(epsilon * N) * xt[(1 + N * p):((N + 1) * p),]
    for(j in 1:p){
      for(k in j:p){
        if (abs(share[j,k]) < abs(share[k,j])){
          share[j,k] = share[j,k]
          share[k,j] = share[j,k]
        }
        else{
          share[j,k] = share[k,j]
          share[k,j] = share[k,j]
        }
      }
    }

    out = list(graphs = Graphs, share = share)
    # add names / lables to output precision matrix

    class(out) = "simule"
    out = add_name_to_out(out,X)
    return(out)
}