nonlinear-metrics-speed-compare.md

if (!requireNamespace("piececor")) devtools::install_github('brshallo/piececor')
library(dplyr)

set.seed(1234)
x <- seq(.01, 3*pi, pi/100)
y <- sin(x) + rnorm(length(x), sd = 0.25)
xy <- tibble::tibble(x = x, y = y)
xy_dist <- infotheo::discretize(xy)

pcor_value <- function(data){
  piececor::piecewise_cors(data, .target = y, x) %>% 
    piececor::weighted_abs_mean_cors() %>% 
    {.$value}
}

bench::mark(
  mutinfo = infotheo::multiinformation(xy_dist),
  distcor = correlation::correlation(xy, method = "distance"),
  nns = NNS::NNS.dep(x, y)$Dependence,
  nlcor = nlcor::nlcor(x, y, plt = FALSE),
  piececor = pcor_value(xy),
  
  # Predictive power score is the most different from notions of correlation
  # ... maybe shouldn't be on this list
  pps = ppsr::score(xy, x = "x", y = "y", algorithm = "tree")$pps,
  check = FALSE,
  iterations = 10
)
#> Warning: Some expressions had a GC in every iteration; so filtering is disabled.
#> # A tibble: 6 x 6
#>   expression      min   median `itr/sec` mem_alloc `gc/sec`
#>   <bch:expr> <bch:tm> <bch:tm>     <dbl> <bch:byt>    <dbl>
#> 1 mutinfo     369.7us  482.3us  2030.      74.05KB     0   
#> 2 distcor     21.45ms  32.77ms    29.2     25.88MB    11.7 
#> 3 nns        169.69ms 186.06ms     4.72   109.06MB     1.42
#> 4 nlcor      379.35ms 460.94ms     2.12     2.22MB     2.76
#> 5 piececor   424.05ms 447.29ms     2.18    22.49MB     0   
#> 6 pps           1.45s    1.56s     0.620    1.65MB     0