Closed osorensen closed 6 months ago
Almost there now for the modal ranking. Seems correctly implemented but have to add some unit tests.
library(BayesMallows) library(patchwork) library(microbenchmark) mod1 <- compute_mallows( data = setup_rank_data(potato_visual), compute_options = set_compute_options(nmc = 10000, burnin = 1000) ) mod2 <- compute_mallows( data = setup_rank_data(potato_visual), compute_options = set_compute_options( nmc = 10000, burnin = 1000, rho_proposal = "swap") ) assess_convergence(mod1) + assess_convergence(mod2)
assess_convergence(mod1, parameter = "rho", items = 1:5) + assess_convergence(mod2, parameter = "rho", items = 1:5)
plot(mod1) + plot(mod2)
microbenchmark( compute_mallows( data = setup_rank_data(potato_visual) ), compute_mallows( data = setup_rank_data(potato_visual), compute_options = set_compute_options(rho_proposal = "swap") ) ) #> Warning in microbenchmark(compute_mallows(data = #> setup_rank_data(potato_visual)), : less accurate nanosecond times to avoid #> potential integer overflows #> Unit: milliseconds #> expr #> compute_mallows(data = setup_rank_data(potato_visual)) #> compute_mallows(data = setup_rank_data(potato_visual), compute_options = set_compute_options(rho_proposal = "swap")) #> min lq mean median uq max neval cld #> 32.80193 33.46756 36.69203 34.49510 35.74001 78.19069 100 a #> 32.47028 33.41500 36.54135 34.07779 35.49235 80.22909 100 a mod1 <- compute_mallows( data = setup_rank_data(preferences = beach_preferences), compute_options = set_compute_options(nmc = 5000, burnin = 1000) ) mod2 <- compute_mallows( data = setup_rank_data(preferences = beach_preferences), compute_options = set_compute_options(nmc = 5000, burnin = 1000, rho_proposal = "swap") ) assess_convergence(mod1) + assess_convergence(mod2)
microbenchmark( compute_mallows( data = setup_rank_data(preferences = beach_preferences) ), compute_mallows( data = setup_rank_data(preferences = beach_preferences), compute_options = set_compute_options(rho_proposal = "swap") ) ) #> Unit: milliseconds #> expr #> compute_mallows(data = setup_rank_data(preferences = beach_preferences)) #> compute_mallows(data = setup_rank_data(preferences = beach_preferences), compute_options = set_compute_options(rho_proposal = "swap")) #> min lq mean median uq max neval cld #> 480.3424 487.5387 495.5723 492.2454 496.9240 591.0838 100 a #> 478.5248 488.7884 498.2841 493.1363 499.1616 595.9734 100 a
Created on 2024-02-21 with reprex v2.1.0
Almost there now for the modal ranking. Seems correctly implemented but have to add some unit tests.
Created on 2024-02-21 with reprex v2.1.0