R/perf_eval.R

In b2slab/diffuStats: Diffusion scores on biological networks

#' Compute performance of diffusion scores on a single case
#'
#' Function \code{perf_eval} directly compares a desired output
#' with the scores from diffusion. It handles the possible
#' shapes of the scores (vector, matrix, list of matrices)
#' and gives the desired metrics.
#'
#'
#' @param prediction smoothed scores; either a named numeric vector,
#' a column-wise matrix whose rownames are nodes and colnames are
#' different scores, or a named list of such matrices.
#' @param validation target scores to which the smoothed scores
#' will be compared to. Must have the same format as the input scores,
#' although the number of rows may vary and only the matching rows will
#' give a performance measure.
#' @param metric named list of metrics to apply. Each metric should accept
#' the form \code{f(actual, predicted)}
#'
#' @return A data frame containing the metrics for each comparable
#' pair of output-validation.
#'
#' @examples
#' # Using a matrix with four set of scores
#' # called Single, Row, Small_sample, Large_sample
#' data(graph_toy)
#' diff <- diffuse(
#'     graph = graph_toy,
#'     scores = graph_toy$input_mat,
#'     method = "raw")
#' df_perf <- perf_eval(
#'     prediction = diff,
#'     validation = graph_toy$input_mat)
#' df_perf
#'
#' @import plyr
# ' @import magrittr
#' @export
perf_eval <- function(
    prediction,
    validation,
    metric = list(auc = metric_fun(curve = "ROC"))
    ) {

    # find out the input format
    format_pred <- which_format(prediction)
    format_val <- which_format(validation)
    if (format_pred != format_val) {
        stop(
            "'prediction' is ", format_pred,
            " but 'validation' is ", format_val)
    }

    # we convert everything to the most general list format
    # we will undo it in the end
    pred <- to_list(prediction)
    val <- to_list(validation)

    # sanity check
    .check_scores(pred)
    .check_scores(val)
    .check_metric(metric)

    ans <- plyr::ldply(
        # iterate over backgrounds
        names(pred),
        function(bkgd) {
            # common names for metric computation...
            # we have to compare the same nodes!
            common_names <- intersect(
                rownames(val[[bkgd]]),
                rownames(pred[[bkgd]]))
            col_names <- colnames(val[[bkgd]])
            if (any(col_names != colnames(pred)))
                stop(
                    "Column names in background ", bkgd, " differ. ",
                    "Please make sure they are the same columns.")

            # Comparable matrices
            mat_val <- val[[bkgd]][common_names, , drop = FALSE]
            mat_pred <- pred[[bkgd]][common_names, , drop = FALSE]

            # iterate over columns to compute metrics
            df_values <- plyr::ldply(
                col_names,
                function(colname) {
                    # iterate over metrics
                    df_met <- plyr::llply(
                        metric,
                        function(metric_fun) {
                            do.call(
                                metric_fun,
                                list(
                                    mat_val[, colname],
                                    mat_pred[, colname]))
                        }
                    )
                    # metrics data frame with column name
                    data.frame(
                        df_met,
                        Column = colname,
                        stringsAsFactors = FALSE)
                }
            )
            # metrics data frame with bkgd and column name
            df_values$Background <- bkgd
            df_values
        }
    )

    # Delete unnecessary columns
    if (format_pred %in% c("vector", "matrix")) ans$Background <- NULL
    if (format_pred == "vector") ans$Column <- NULL

    return(ans)
}