tensor_predictors/tensorPredictors/R/CISE.R

#' Coordinate-Independent Sparce Estimation.
#'
#' Solves penalized version of a GEP (Generalized Eigenvalue Problem)
#' \deqn{M V = N \Lambda V}
#' with \eqn{\Lambda} a matrix with eigenvalues on the main diagonal and \eqn{V}
#' are the first \eqn{d} eigenvectors.
#'
#' TODO: DOES NOT WORK, DON'T KNOW WHY (contact first author for the Matlab code)
#'
#' @param M is the GEP's left hand side
#' @param N is the GEP's right hand side
#' @param d number of leading eigenvalues, -vectors to be computed
#' @param max.iter maximum number of iterations for iterative optimization
#' @param Theta Penalty parameter, if not provided an reasonable estimate for
#'  a grid of parameters is computed. If Theta is a vector (or number), the
#'  provided values of Theta are used as penalty parameter candidates.
#' @param tol.norm numerical tolerance for dropping rows
#' @param tol.break break condition tolerance
#'
#' @returns a list
#'
#' @examples \dontrun{
#' # Study 1-4 from CISE paper
#' dataset <- function(name, n = 60, p = 24) {
#'     name <- toupper(name)
#'     if (!startsWith('M', name)) { name <- paste0('M', name) }
#' 
#'     if (name %in% c('M1', 'M2', 'M3')) {
#'         Sigma <- 0.5^abs(outer(1:p, 1:p, `-`))
#'         X <- rmvnorm(n, sigma = Sigma)
#'         y <- switch(name,
#'             M1 = rowSums(X[, 1:3]) + rnorm(n, 0, 0.5),
#'             M2 = rowSums(X[, 1:3]) + rnorm(n, 0, 2),
#'             M3 = X[, 1] / (0.5 + (X[, 2] + 1.5)^2) + rnorm(n, 0, 0.2)
#'         )
#'         B <- switch(name,
#'             M1 = as.matrix(as.double(1:p < 4)),
#'             M2 = as.matrix(as.double(1:p < 4)),
#'             M3 = diag(1, p, 2)
#'         )
#'     } else if (name == 'M4') {
#'         y <- rnorm(n)
#'         Delta <- 0.5^abs(outer(1:p, 1:p, `-`))
#'         Gamma <- 0.5 * cbind(
#'             (1:p <= 4),
#'             (-(1:p <= 4))^(1:p + 1)
#'         )
#'         B <- qr.Q(qr(solve(Delta, Gamma)))
#'         X <- cbind(y, y^2) %*% t(Gamma) +
#'             matpow(Delta, 0.5) %*% rmvnorm(n, rep(0, p))
#'     } else {
#'         stop('Unknown dataset name.')
#'     }
#' 
#'     list(X = X, y = y, B = B)
#' }
#' # Sample dataset
#' n <- 1000
#' ds <- dataset(3, n = n)
#' # Convert to PFC associated GEP
#' Fy <- with(ds, cbind(abs(y), y, y^2))
#' P.Fy <- Fy %*% solve(crossprod(Fy), t(Fy))
#' M <- with(ds, crossprod(X, P.Fy %*% X) / nrow(X))   # Sigma Fit
#' N <- cov(ds$X)                                      # Sigma
#' 
#' fits <- CISE(M, N, d = ncol(ds$B), Theta = log(seq(1, exp(1e-3), len = 1000)))
#' 
#' BIC <- unlist(Map(attr, fits, 'BIC'))
#' df <- unlist(Map(attr, fits, 'df'))
#' dist <- unlist(Map(attr, fits, 'dist'))
#' iter <- unlist(Map(attr, fits, 'iter'))
#' theta <- unlist(Map(attr, fits, 'theta'))
#' p.theta <- unlist(Map(function(V) sum(rowSums(V^2) > 1e-9), fits))
#' 
#' par(mfrow = c(2, 2))
#' plot(theta, BIC, type = 'l')
#' plot(theta, p.theta, type = 'l')
#' plot(theta, dist, type = 'l')
#' plot(theta, iter, type = 'l')
#' }
#'
#' @seealso "Coordinate-Independent Sparse Sufficient Dimension
#' Reduction and Variable Selection" By Xin Chen, Changliang Zou and
#' R. Dennis Cook.
#'
#' @suggest RSpectra
#'
#' @export
CISE <- function(M, N, d = 1L, method = "PFC", max.iter = 100L, Theta = NULL,
    tol.norm = 1e-6, tol.break = 1e-6, r = 0.5
) {

    isrN <- matpow(N, -0.5)     # N^-1/2 ... Inverse Square-Root of N
    G <- isrN %*% M %*% isrN    # G = N^-1/2 M N^-1/2

    # Step 1: Solve (ordinary, unconstraint) eigenvalue problem used as an
    # initial value for following iterative optimization (Solution of (2.8))
    Gamma <- if (requireNamespace("RSpectra", quietly = TRUE)) {
        RSpectra::eigs_sym(G, d)$vectors
    } else {
        eigen(G, symmetric = TRUE)$vectors[, d, drop = FALSE]
    }
    V.init <- isrN %*% Gamma

    # Build penalty grid
    if (missing(Theta)) {
        # TODO: figure out what a good min to max in steps grid is
        theta.max <- sqrt(max(rowSums(Gamma^2)))
        Theta <- seq(0.01 * theta.max, 0.75 * theta.max, length.out = 10)
    }

    norms <- sqrt(rowSums(V.init^2)) # row norms of V
    theta.scale <- 0.5 * ifelse(norms < tol.norm, 0, norms^(-r))

    # For each penalty candidate
    fits <- lapply(Theta, function(theta) {
        # Step 2: Iteratively optimize constraint GEP
        V <- V.init
        dropped <- rep(FALSE, nrow(M))  # Keep track of dropped variables
        for (iter in seq_len(max.iter)) {
            # Compute current row norms
            norms <- sqrt(rowSums(V^2)) # row norms of V
            # Check if variables are dropped. If so, update dropped and
            # recompute the inverse square root of N
            if (any(norms < tol.norm)) {
                dropped[!dropped] <- norms < tol.norm
                norms <- norms[!(norms < tol.norm)]
                isrN <- matpow(N[!dropped, !dropped], -0.5)
            }
            # Approx. penalty term derivative at current position
            h <- theta * (theta.scale[!dropped] / norms)

            # Updated G at current position (scaling by 1/2 done in `theta.scale`)
            A <- G[!dropped, !dropped] - (isrN %*% (h * isrN))
            # Solve next iteration GEP
            Gamma <- if (requireNamespace("RSpectra", quietly = TRUE)) {
                RSpectra::eigs_sym(A, d)$vectors
            } else {
                eigen(A, symmetric = TRUE)$vectors[, d, drop = FALSE]
            }
            V.last <- V
            V <- isrN %*% Gamma

            # Check if there are enough variables left
            if (nrow(V) < d + 1) {
                break
            }
            # Break dondition (only when nothing dropped)
            if (nrow(V.last) == nrow(V)
            && dist.subspace(V.last, V, normalize = TRUE) < tol.break) {
                break
            }
        }

        # Recreate dropped variables and fill parameters with 0.
        V.full <- matrix(0, nrow(M), d)
        V.full[!dropped, ] <- V

        # df <- (sum(!dropped) - d) * d
        # BIC <- -sum(V * (M %*% V)) + log(n) * df / n
        # cat("theta:",  sprintf('%7.3f', range(theta)),
        #     "- iter:", sprintf('%3d', iter),
        #     "- df:", sprintf('%3d', df),
        #     "- BIC:", sprintf('%7.3f', BIC),
        #     "- dist:", sprintf('%7.3f', dist.subspace(V.last, V, normalize = TRUE)),
        #     # "- ", paste(sprintf('%6.2f', norms), collapse = ", "),
        #     '\n')

        structure(qr.Q(qr(V.full)),
            theta = theta, iter = iter, BIC = BIC, df = df,
            dist = dist.subspace(V.last, V, normalize = TRUE))
    })

    structure(fits,
        call = match.call(),
        class = c("tensorPredictors", "CISE"))
}