R/s_curve.R

In dselivanov/LSHR: Locality Sensitive Hashing in R

#' @name get_s_curve
#' @title Calculates/plots "S-curve"
#' @description Calculates "S-curve". This is used to pick number of hash functions and bands which
#' provides tradeoff between precision and recall for approximate near neighbor search.
#' @param number_hashfun guess about number of hash functions to use
#' @param n_bands_min don't plot s-curves for number of bands less than \code{n_bands_min}
#' @param n_rows_per_band_min don't plot s-curves for number of rows less than \code{n_rows_per_band_min}
#' @param plot logical, whether to plot s-curves.
#' @export
get_s_curve <- function(number_hashfun,
                        n_bands_min = 1,
                        n_rows_per_band_min = 1,
                        plot = interactive()) {

  s = seq(0.5, 1, 0.005)

  bands_number <- divisors(number_hashfun)
  rows_per_band <- number_hashfun / bands_number
  i <- bands_number >= n_bands_min & rows_per_band >= n_rows_per_band_min

  bands_number <- bands_number[i]
  rows_per_band <- rows_per_band[i]

  s_curve <-
    mapply(function(n_band, n_rows_per_band) {
      data.table(probability_become_candidate = 1 - (1 - s ^ n_rows_per_band) ^ n_band,
                 similarity = s,
                 n_bands = n_band,
                 n_rows_per_band = n_rows_per_band)
                 #setup = paste0("bands=", n_band, ";rows_per_band=", n_rows_per_band))
    }, bands_number, rows_per_band, SIMPLIFY = F)

  s_curve = rbindlist(s_curve)
  g = NULL
  if (plot) {
    if(require(ggplot2)) {
      g =
        ggplot(s_curve) +
        geom_line(aes(x = similarity,
                      y = probability_become_candidate,
                      col = interaction(n_bands, n_rows_per_band, sep = " : " ))) +
        scale_color_discrete("bands_number : rows_per_band")
      print(g)
    } else {
      warning("install ggplot2 in order to plot curves")
    }
  }
  attr(s_curve, "ggplot") = g
  invisible(s_curve)
}