#' Feature Selection Optimization for sPCA method 

#'

#' This function identify the number of feautures to keep per component and thus by cluster in \code{mixOmics::spca} 

#' by optimizing the silhouette coefficient, which assesses the quality of clustering.

#' 

#' @param X numeric matrix (or data.frame) with features in columns and samples in rows

#' @param ncomp integer, number of component to include in the model

#' @param test.keepX vector of integer containing the different value of keepX to test for block \code{X}.

#' @param ... other parameters to be included in the spls model (see \code{mixOmics::spca})

#' 

#' @return 

#' \item{silhouette}{silhouette coef. computed for every combinasion of keepX/keepY}

#' \item{ncomp}{number of component included in the model}

#' \item{test.keepX}{list of tested keepX}

#' \item{block}{names of blocks}

#' \item{slopes}{"slopes" computed from the silhouette coef. for each keepX and keepY, used to determine the best keepX and keepY}

#' \item{choice.keepX}{best \code{keepX} for each component}

#' 

#' @details

#' For each component and for each keepX value, a spls is done from these parameters.

#' Then the clustering is performed and the silhouette coefficient is calculated for this clustering.

#'

#' We then calculate "slopes" where keepX are the coordinates and the silhouette is the intensity.

#' A z-score is assigned to each slope.

#' We then identify the most significant slope which indicates a drop in the silhouette coefficient and thus a deterioration of the clustering.

#'

#' 

#' @examples

#' demo <- suppressWarnings(get_demo_cluster())

#' X <- demo$X

#' 

#' # tuning

#' tune.spca.res <- tuneCluster.spca(X = X, ncomp = 2, test.keepX = c(2:10))

#' keepX <- tune.spca.res$choice.keepX

#' plot(tune.spca.res)

#' 

#' # final model

#' spca.res <- mixOmics::spca(X=X, ncomp = 2, keepX = keepX)

#' plotLong(spca.res)

#' @import mixOmics

#' @importFrom dplyr left_join

#' @importFrom dplyr mutate

#' @importFrom dplyr filter

#' @export

tuneCluster.spca <- function(X, ncomp = 2, test.keepX = rep(ncol(X), ncomp), ...){

    #-- checking input parameters ---------------------------------------------#

    #--------------------------------------------------------------------------#

    #-- X

    X <- validate_matrix_X(X)

    #-- ncomp

    ncomp <- validate_ncomp(ncomp, list(X))

    #-- test.keepX

    test.keepX <- validate_test_keepX(test.keepX = test.keepX, X = X)

    min.test.keepX <- rep(min(test.keepX), ncomp)

    #-- launch tuning  --------------------------------------------------------#

    #--------------------------------------------------------------------------#

    #-- 0. set output object

    result <- as.data.frame(matrix(ncol = 4, nrow = length(test.keepX)*ncomp))

    colnames(result) <- c("comp", "X", "pos", "neg")

    result.index <- 0

    #-- 1. compute dissimilarity matrix for silhouette coef. (once and for all)

    dmatrix <- dmatrix.spearman.dissimilarity(X)

    cluster <- as.data.frame(list("feature" = rownames(dmatrix)))

    #-- tuning

    for(comp in 1:ncomp){

        tmp.keepX <- min.test.keepX  # foreach comp, keepX of other comp is set to minimum

        for(keepX in test.keepX){

            tmp.keepX[comp] <- keepX

            #-- 2. run spca

            kX = tmp.keepX

            spca.res <- mixOmics::spca(X = X, ncomp = ncomp, keepX = kX)

            #-- 3. extract clusters

            tmp.cluster <- getCluster(spca.res)

            tmp.cluster <- suppressWarnings(dplyr::left_join(cluster, tmp.cluster[c(1,4)],

                                                             by = c("feature"="molecule"))) %>%

                dplyr::mutate(cluster = as.numeric(as.character(cluster))) %>%

                dplyr::mutate(cluster = ifelse(is.na(cluster), 0, cluster))

            #-- 4. compute silhouette

            sil <- silhouette(dmatrix, tmp.cluster$cluster)

            #-- 6. store

            result.index <- result.index + 1

            result[result.index, "comp"] <- comp

            result[result.index, "X"] <- kX[comp]

            pos.res <-  sil$average.cluster  %>% 

                dplyr::filter(cluster == comp) %>%

                dplyr::pull(silhouette.coef)

            result[result.index, "pos"] <- ifelse(length(pos.res) == 0, NA, pos.res)

            neg.res <-  sil$average.cluster  %>%

                dplyr::filter(cluster == -comp) %>%

                dplyr::pull(silhouette.coef)

            result[result.index, "neg"] <- ifelse(length(neg.res) == 0, NA, neg.res)

        }

    }

    result <- list("silhouette" = result)

    result[["ncomp"]] <- ncomp

    result[["test.keepX"]] <- test.keepX

    result[["block"]] <- c("X")

    class(result) <- "spca.tune.silhouette"

    #-- 7. choice.keepX

    result[["slopes"]] <- tune.silhouette.get_slopes(result)

    tmp <- tune.silhouette.get_choice_keepX(result) # choice keepX/keepY

    result[["choice.keepX"]] <- unlist(lapply(tmp, function(x) x$X))

    return(result)

}

#' @import mixOmics

#' @importFrom dplyr left_join mutate filter group_by top_n

#' @import ggplot2

#' @importFrom tidyr pivot_longer

#' @importFrom ggrepel geom_label_repel

#' @export

plot.spca.tune.silhouette <- function(x, ...){

    #-- checking input parameters ---------------------------------------------#

    #--------------------------------------------------------------------------#

    #-- should be a spca.tune.silhouette" object.

    ncomp <- x$ncomp

    test.keepX <- x$test.keepX

    tmp <- x$silhouette %>% 

        tidyr::pivot_longer(names_to = "contrib", values_to = "value", -c(comp,X)) %>%

        na.omit %>% # remove NA intruced in pos/neg

        dplyr::mutate(comp = as.factor(paste0("Comp ", comp)), 

               contrib = factor(contrib, levels = c("pos","neg"))) %>%

        dplyr::group_by(comp, contrib)

    choice <- list(comp= as.factor(paste0("Comp ",names(x$choice.keepX))), 

                   X = unname(x$choice.keepX)) %>%

        as.data.frame() %>%

        dplyr::left_join(tmp, by = c("comp"="comp", "X"="X")) %>% 

        dplyr::group_by(comp, X) %>%

        dplyr::top_n(n = 1, wt = value)

    choice.vline <- choice %>%

        dplyr::select(c("comp", "X"))

    ggplot.df <- ggplot(tmp, aes(x=X, y =value, col = comp)) + 

        geom_line(aes(lty = contrib)) + facet_wrap(~comp) +

        theme_bw() +

        geom_point(data = choice, size = 5, pch = 18) +

        ggrepel::geom_label_repel(data = choice, aes(label = X), col = "black") +

        scale_color_manual(values = mixOmics::color.mixo(1:x$ncomp)) +

        labs(x ="tested keepX",  y = "Silhouette Coef.", color = "Comp.", lty = "Contrib.") +

        geom_vline(data = choice.vline, aes(xintercept = X), lty = 5, col = "grey")

    print(ggplot.df)

    return(invisible(ggplot.df))

}