From ec2afa6521b56a74a935dcd5aabe0d3b62408ae8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 08:16:11 +0100 Subject: [PATCH 01/41] replaced purrr with lapply in plot_elbow and wrote an extra test --- R/plot_elbow.R | 4 ++-- tests/testthat/test_plot_elbow.R | 5 +++-- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/R/plot_elbow.R b/R/plot_elbow.R index 5cf04871..3b599244 100644 --- a/R/plot_elbow.R +++ b/R/plot_elbow.R @@ -33,7 +33,7 @@ plot_elbow <- function(..., burnin = NULL){ models <- models[[1]] } - df <- purrr::map_dfr(models, function(x) { + df <- do.call(rbind, lapply(models, function(x) { stopifnot(class(x) == "BayesMallows") if(!("burnin" %in% names(x))){ @@ -56,7 +56,7 @@ plot_elbow <- function(..., burnin = NULL){ df <- dplyr::mutate(df, n_clusters = x$n_clusters) return(df) - }) + })) ggplot2::ggplot(df, ggplot2::aes(x = as.factor(.data$n_clusters), y = .data$value)) + ggplot2::geom_boxplot() + diff --git a/tests/testthat/test_plot_elbow.R b/tests/testthat/test_plot_elbow.R index 3beed9ea..4ab146d8 100644 --- a/tests/testthat/test_plot_elbow.R +++ b/tests/testthat/test_plot_elbow.R @@ -2,11 +2,12 @@ context("Testing plot_elbow") test_that("plot_elbow fails when it should", { test <- compute_mallows_mixtures(n_clusters = 1:3, rankings = potato_visual, - nmc = 200, include_wcd = TRUE) + nmc = 20, include_wcd = TRUE) + expect_s3_class(plot_elbow(test, burnin = 16), "ggplot") expect_error(plot_elbow(test, burnin = 300)) expect_error(plot_elbow(test, burnin = 200)) expect_error(plot_elbow(test)) test <- compute_mallows_mixtures(n_clusters = 1:3, rankings = potato_visual, - nmc = 200, include_wcd = FALSE) + nmc = 20, include_wcd = FALSE) expect_error(plot_elbow(test, burnin = 100)) }) From 08d398cb1bedb94acfb97bd7505f2680f08a946d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 08:30:41 +0100 Subject: [PATCH 02/41] change purrr with Map in sample_mallows --- R/sample_mallows.R | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/R/sample_mallows.R b/R/sample_mallows.R index 144779be..848822fc 100644 --- a/R/sample_mallows.R +++ b/R/sample_mallows.R @@ -97,13 +97,14 @@ sample_mallows <- function(rho0, alpha0, n_samples, lag.max = max_lag, plot = FALSE, demean = TRUE) names(autocorr) <- items_to_plot - - autocorr <- purrr::map_dfr(autocorr, function(x) { - dplyr::tibble( + autocorr <- do.call(rbind, Map(function(x, xnm){ + data.frame( + item = xnm, acf = x$acf[, 1, 1], lag = x$lag[, 1, 1] ) - }, .id = "item") + }, x = autocorr, xnm = names(autocorr))) + autocorr <- dplyr::mutate(autocorr, item = as.factor(as.integer(.data$item))) ac_plot <- ggplot2::ggplot(autocorr, From 0bdf65003097893ba02169cbaf13c00e100a307a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 08:32:33 +0100 Subject: [PATCH 03/41] replaced purrr with lapply in compute_mallows_mixtures --- R/compute_mallows_mixtures.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/compute_mallows_mixtures.R b/R/compute_mallows_mixtures.R index 738518c7..68c03dbd 100644 --- a/R/compute_mallows_mixtures.R +++ b/R/compute_mallows_mixtures.R @@ -27,7 +27,7 @@ compute_mallows_mixtures <- function(n_clusters, ..., cl = NULL){ stopifnot(is.null(cl) || inherits(cl, "cluster")) if(is.null(cl)){ - models <- purrr::map(n_clusters, function(x) { + models <- lapply(n_clusters, function(x) { compute_mallows(..., n_clusters = x) }) } else { From 0233d6de9025ae7141feb5e6b1f237c06b2c58a3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 08:35:26 +0100 Subject: [PATCH 04/41] removed purrr from compute_posterior_intervals --- R/compute_posterior_intervals.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/compute_posterior_intervals.R b/R/compute_posterior_intervals.R index 2d07cda8..4b471e1a 100644 --- a/R/compute_posterior_intervals.R +++ b/R/compute_posterior_intervals.R @@ -90,7 +90,7 @@ compute_posterior_intervals <- function(model_fit, burnin = model_fit$burnin, # Find contiguous regions breaks <- c(0, which(diff(values) != 1), length(values)) - hpdi <- purrr::map(seq(length(breaks) - 1), function(.x, values, breaks) { + hpdi <- lapply(seq(length(breaks) - 1), function(.x, values, breaks) { vals <- values[(breaks[.x] + 1):breaks[.x + 1]] vals <- unique(c(min(vals), max(vals))) paste0("[", paste(vals, collapse = ","), "]") From 50274faf1db4cbb9df3654b41c58d9ffd67ccde4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 08:39:03 +0100 Subject: [PATCH 05/41] removed purrr from assess_convergence --- R/assess_convergence.R | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/R/assess_convergence.R b/R/assess_convergence.R index f491c9cf..62aac0be 100644 --- a/R/assess_convergence.R +++ b/R/assess_convergence.R @@ -36,11 +36,11 @@ assess_convergence <- function(model_fit, parameter = "alpha", items = NULL, m <- model_fit$alpha trace_alpha(m, FALSE) } else if(inherits(model_fit, "BayesMallowsMixtures")){ - m <- purrr::map_dfr(model_fit, function(x){ + m <- do.call(rbind, lapply(model_fit, function(x){ dplyr::mutate(x$alpha, cluster = as.character(.data$cluster), n_clusters = x$n_clusters) - }) + })) trace_alpha(m, TRUE) } @@ -48,7 +48,7 @@ assess_convergence <- function(model_fit, parameter = "alpha", items = NULL, if(inherits(model_fit, "BayesMallows")){ trace_rho(model_fit, items) } else if(inherits(model_fit, "BayesMallowsMixtures")){ - cowplot::plot_grid(plotlist = purrr::map(model_fit, trace_rho, clusters = TRUE, items = items)) + cowplot::plot_grid(plotlist = lapply(model_fit, trace_rho, clusters = TRUE, items = items)) } } else if(parameter == "Rtilde") { @@ -62,11 +62,11 @@ assess_convergence <- function(model_fit, parameter = "alpha", items = NULL, if(inherits(model_fit, "BayesMallows")){ m <- model_fit$cluster_probs } else if(inherits(model_fit, "BayesMallowsMixtures")){ - m <- purrr::map_dfr(model_fit, function(x){ + m <- do.call(rbind, lapply(model_fit, function(x){ dplyr::mutate(x$cluster_probs, cluster = as.character(.data$cluster), n_clusters = x$n_clusters) - }) + })) } trace_cluster_probs(m) From 53ddf65f90e6bcce1ae7fc71a5f09da60b6b38b4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 08:49:25 +0100 Subject: [PATCH 06/41] removed purrr from rank conversion --- R/rank_conversion.R | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/R/rank_conversion.R b/R/rank_conversion.R index 8bc4d8e9..0918bc4a 100644 --- a/R/rank_conversion.R +++ b/R/rank_conversion.R @@ -52,16 +52,17 @@ create_ranking <- function(orderings){ n_items <- ncol(orderings) # Convert to list, for easier functional programming - orderings <- purrr::array_branch(orderings, margin = 1) + orderings <- split(orderings, f = seq_len(nrow(orderings))) # Check that matrix contains permutations - check <- purrr::map_lgl(orderings, validate_permutation) - if(!all(check)){ + check <- lapply(orderings, validate_permutation) + + if(!Reduce(`&&`, check)){ stop(paste("orderings must contain proper permutations. Problem row(s):", which(!check))) } # Convert each ordering to ranking, taking special care of missing values - rankings <- purrr::map(orderings, function(x) { + rankings <- lapply(orderings, function(x) { # Find out which items are missing missing_items <- setdiff(1:n_items, x) # Possible rankings for each item From 5f42341c06258637d244cd129d29615e94746878 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 09:01:42 +0100 Subject: [PATCH 07/41] replaced purrr with vapply et al in print function and added test --- R/print.BayesMallowsMixtures.R | 4 ++-- tests/testthat/test_print.R | 19 +++++++++++++------ 2 files changed, 15 insertions(+), 8 deletions(-) diff --git a/R/print.BayesMallowsMixtures.R b/R/print.BayesMallowsMixtures.R index 7202e7e6..ac874384 100644 --- a/R/print.BayesMallowsMixtures.R +++ b/R/print.BayesMallowsMixtures.R @@ -16,11 +16,11 @@ print.BayesMallowsMixtures <- function(x, ...){ # issue a warning. This is because print.BayesMallowsMixtures must have the same # required arguments as base::print. - if(!all(purrr::map_lgl(x, ~ inherits(.x, "BayesMallows")))) { + if(!Reduce(`&`, lapply(x, function(x) inherits(x, "BayesMallows")))) { stop("All elements of a BayesMallowsMixtures object must be of class BayesMallows.") } - n_clusters <- purrr::map_int(x, ~ .x$n_clusters) + n_clusters <- vapply(x, function(x) x$n_clusters, integer(1)) cat("Collection of", length(x), "Bayesian Mallows Mixture Models with the following number of mixture components:\n", paste0(paste(n_clusters, collapse = ", "), ".")) diff --git a/tests/testthat/test_print.R b/tests/testthat/test_print.R index 857b8cc5..0aff5bd8 100644 --- a/tests/testthat/test_print.R +++ b/tests/testthat/test_print.R @@ -3,18 +3,25 @@ context("Testing print.BayesMallows and print.BayesMallowsMixtures") test_that("print.BayesMallows fails when it should", { class(mtcars) <- "BayesMallows" expect_error(print(mtcars)) - m <- compute_mallows(potato_visual, nmc = 100) + m <- compute_mallows(potato_visual, nmc = 5) m$n_items <- NULL - expect_error(print(m)) + expect_error(print(m), "BayesMallows object must have elements n_items and n_assessors.") m$n_assessors <- NULL - expect_error(print(m)) + expect_error(print(m), + "BayesMallows object must have elements n_items and n_assessors.") }) test_that("print.BayesMallowsMixtures fails when it should", { class(mtcars) <- "BayesMallowsMixtures" - expect_error(print(mtcars)) - m <- compute_mallows_mixtures(potato_visual, n_clusters = 1:3, nmc = 100) + expect_error(print(mtcars), + regexp = "All elements of a BayesMallowsMixtures object must be of class BayesMallows.") + m <- compute_mallows_mixtures(potato_visual, n_clusters = 1:3, nmc = 5) class(m[[1]]) <- "list" - expect_error(print(m)) + expect_error(print(m), + regexp = "All elements of a BayesMallowsMixtures object must be of class BayesMallows.") + + m <- compute_mallows_mixtures(potato_visual, n_clusters = 1:3, nmc = 5) + expect_output(print(m), + "Collection of 3 Bayesian Mallows Mixture Models with the following number of mixture components:") }) From bab13e8c98834c61243e98a0769bd941dcb5b1e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 09:10:52 +0100 Subject: [PATCH 08/41] removed purrr from compute_mallows --- R/compute_mallows.R | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/R/compute_mallows.R b/R/compute_mallows.R index 2a5992a7..e3131a18 100644 --- a/R/compute_mallows.R +++ b/R/compute_mallows.R @@ -313,8 +313,8 @@ compute_mallows <- function(rankings = NULL, n_items <- max(c(preferences$bottom_item, preferences$top_item)) n_assessors <- length(unique(preferences$assessor)) if(is.null(rankings)){ - rankings <- purrr::rerun(n_assessors, sample(x = n_items, size = n_items)) - rankings <- matrix(unlist(rankings), ncol = n_items, nrow = n_assessors, byrow = TRUE) + rankings <- replicate(n_assessors, sample(x = n_items, size = n_items), simplify = "numeric") + rankings <- matrix(rankings, ncol = n_items, nrow = n_assessors, byrow = TRUE) } } @@ -325,16 +325,15 @@ compute_mallows <- function(rankings = NULL, if(any(is.na(rankings))){ dn <- dimnames(rankings) - rankings <- purrr::map(purrr::array_branch(rankings, margin = 1), + rankings <- lapply(split(rankings, f = seq_len(nrow(rankings))), function(x) { if(sum(is.na(x)) == 1) x[is.na(x)] <- setdiff(1:length(x), x) return(x) }) - rankings <- t(matrix(unlist(rankings), nrow = n_items)) + rankings <- do.call(rbind, rankings) dimnames(rankings) <- dn } - if(!is.null(rho_init)) { if(!validate_permutation(rho_init)) stop("rho_init must be a proper permutation") if(!(sum(is.na(rho_init)) == 0)) stop("rho_init cannot have missing values") From 34c34bdd5e966b9ec3f1f8fd5d1e5bf7fb775525 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 09:13:37 +0100 Subject: [PATCH 09/41] removed purrr from generate_constraints and from a test --- R/generate_constraints.R | 4 ++-- tests/testthat/test_rank_freq_distr.R | 7 +++++-- 2 files changed, 7 insertions(+), 4 deletions(-) diff --git a/R/generate_constraints.R b/R/generate_constraints.R index 087b9276..264f1777 100644 --- a/R/generate_constraints.R +++ b/R/generate_constraints.R @@ -52,7 +52,7 @@ constraint_fun <- function(x, n_items){ items_above <- split(items_above, items_above[["bottom_item"]]) # For each item, find which items are ranked above it - items_above <- purrr::map(items_above, function(x) { + items_above <- lapply(items_above, function(x) { res <- unique(x[["top_item"]]) res <- res[!is.na(res)] }) @@ -66,7 +66,7 @@ constraint_fun <- function(x, n_items){ items_below <- split(items_below, items_below[["top_item"]]) # For each item, find which items are ranked above it - items_below <- purrr::map(items_below, function(x) { + items_below <- lapply(items_below, function(x) { res <- unique(x[["bottom_item"]]) res <- res[!is.na(res)] }) diff --git a/tests/testthat/test_rank_freq_distr.R b/tests/testthat/test_rank_freq_distr.R index b5f3fd02..53e81fda 100644 --- a/tests/testthat/test_rank_freq_distr.R +++ b/tests/testthat/test_rank_freq_distr.R @@ -25,8 +25,11 @@ test_that("rank_freq_distr works", { ) set.seed(9988) - rows <- unlist(purrr::map2( - sample(20:100, nrow(potato_visual), replace = TRUE), 1:12, ~ rep(.y, each = .x))) + rows <- unlist(Map(function(x, y){ + rep(y, each = x) + }, + x = sample(20:100, nrow(potato_visual), replace = TRUE), + y = 1:12)) mat <- potato_visual[rows, ] expect_equal( rank_freq_distr(rankings = mat), From 611e64b70b788d4ed21e2e9b959491f96cbad94f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 09:14:50 +0100 Subject: [PATCH 10/41] removed purrr from Imports and incremented development version --- DESCRIPTION | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index e91cc5a3..7ccacc7d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: BayesMallows Type: Package Title: Bayesian Preference Learning with the Mallows Rank Model -Version: 1.0.4 +Version: 1.0.4.9000 Authors@R: c(person("Oystein", "Sorensen", email = "oystein.sorensen.1985@gmail.com", role = c("aut", "cre"), @@ -51,7 +51,6 @@ Imports: Rcpp (>= 1.0.0), sets (>= 1.0-18), relations (>= 0.6-8), tidyr (>= 1.1.1), - purrr (>= 0.3.0), rlang (>= 0.3.1), PerMallows (>= 1.13), HDInterval (>= 0.2.0), From a50d9912d38d07aab1310deb2f7b06f0e75e67bb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 09:27:30 +0100 Subject: [PATCH 11/41] removed library call to purrr --- inst/examples/obs_freq_example.R | 2 +- man/obs_freq.Rd | 2 +- tests/testthat/test_compute_mallows.R | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/inst/examples/obs_freq_example.R b/inst/examples/obs_freq_example.R index 5ca332c7..d533f8cf 100644 --- a/inst/examples/obs_freq_example.R +++ b/inst/examples/obs_freq_example.R @@ -1,6 +1,6 @@ library(dplyr) library(tidyr) -library(purrr) + # The first example uses full rankings in the potato_visual dataset, but we assume # that each row in the data corresponds to between 100 and 500 assessors. set.seed(1234) diff --git a/man/obs_freq.Rd b/man/obs_freq.Rd index a05de3c5..cbbd05f0 100644 --- a/man/obs_freq.Rd +++ b/man/obs_freq.Rd @@ -14,7 +14,7 @@ the observation frequencies. \examples{ library(dplyr) library(tidyr) -library(purrr) + # The first example uses full rankings in the potato_visual dataset, but we assume # that each row in the data corresponds to between 100 and 500 assessors. set.seed(1234) diff --git a/tests/testthat/test_compute_mallows.R b/tests/testthat/test_compute_mallows.R index bc314582..cd4dad6f 100644 --- a/tests/testthat/test_compute_mallows.R +++ b/tests/testthat/test_compute_mallows.R @@ -1,6 +1,6 @@ library(dplyr) library(tidyr) -library(purrr) + context("Testing compute_mallows") test_that("miscellaneous input validation", { From ad755f7a839b1b52724efca8f498a4e3b39882a6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 09:33:11 +0100 Subject: [PATCH 12/41] replaced map with lapply in example --- inst/examples/obs_freq_example.R | 2 +- man/obs_freq.Rd | 2 +- tests/testthat/test_compute_mallows.R | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/inst/examples/obs_freq_example.R b/inst/examples/obs_freq_example.R index d533f8cf..aa6ee517 100644 --- a/inst/examples/obs_freq_example.R +++ b/inst/examples/obs_freq_example.R @@ -70,7 +70,7 @@ obs_freq <- sample(x = 1:4, size = length(unique(beach_preferences$assessor)), r # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times beach_pref_rep <- beach_preferences %>% - mutate(new_assessor = map(obs_freq[assessor], ~ 1:.x)) %>% + mutate(new_assessor = lapply(obs_freq[assessor], ~ 1:.x)) %>% unnest(cols = new_assessor) %>% mutate(assessor = paste(assessor, new_assessor, sep = ",")) %>% select(-new_assessor) diff --git a/man/obs_freq.Rd b/man/obs_freq.Rd index cbbd05f0..ac1f77d5 100644 --- a/man/obs_freq.Rd +++ b/man/obs_freq.Rd @@ -84,7 +84,7 @@ obs_freq <- sample(x = 1:4, size = length(unique(beach_preferences$assessor)), r # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times beach_pref_rep <- beach_preferences \%>\% - mutate(new_assessor = map(obs_freq[assessor], ~ 1:.x)) \%>\% + mutate(new_assessor = lapply(obs_freq[assessor], ~ 1:.x)) \%>\% unnest(cols = new_assessor) \%>\% mutate(assessor = paste(assessor, new_assessor, sep = ",")) \%>\% select(-new_assessor) diff --git a/tests/testthat/test_compute_mallows.R b/tests/testthat/test_compute_mallows.R index cd4dad6f..d3a4d662 100644 --- a/tests/testthat/test_compute_mallows.R +++ b/tests/testthat/test_compute_mallows.R @@ -120,7 +120,7 @@ test_that("compute_mallows treats obs_freq properly",{ # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times beach_pref_rep <- beach_small %>% - mutate(new_assessor = map(obs_freq[assessor], ~ 1:.x)) %>% + mutate(new_assessor = lapply(obs_freq[assessor], ~ 1:.x)) %>% unnest(cols = new_assessor) %>% mutate(assessor = paste(assessor, new_assessor, sep = ",")) %>% select(-new_assessor) From 12750a2c8387b4ac04ede4d950f17b8fec96ba47 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 09:39:09 +0100 Subject: [PATCH 13/41] removed another call to map --- inst/examples/obs_freq_example.R | 4 ++-- man/obs_freq.Rd | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/inst/examples/obs_freq_example.R b/inst/examples/obs_freq_example.R index aa6ee517..08b0c902 100644 --- a/inst/examples/obs_freq_example.R +++ b/inst/examples/obs_freq_example.R @@ -8,7 +8,7 @@ set.seed(1234) obs_freq <- sample(x = seq(from = 100L, to = 500L, by = 1L), size = nrow(potato_visual), replace = TRUE) # We also create a set of repeated indices, used to extend the matrix rows -repeated_indices <- unlist(map2(1:nrow(potato_visual), obs_freq, ~ rep(.x, each = .y))) +repeated_indices <- unlist(Map(function(x, y) rep(x, each = y), 1:nrow(potato_visual), obs_freq)) # The potato_repeated matrix consists of all rows repeated corresponding to # the number of assessors in the obs_freq vector. This is how a large dataset # would look like without using the obs_freq argument @@ -70,7 +70,7 @@ obs_freq <- sample(x = 1:4, size = length(unique(beach_preferences$assessor)), r # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times beach_pref_rep <- beach_preferences %>% - mutate(new_assessor = lapply(obs_freq[assessor], ~ 1:.x)) %>% + mutate(new_assessor = lapply(obs_freq[assessor], function(x) 1:x)) %>% unnest(cols = new_assessor) %>% mutate(assessor = paste(assessor, new_assessor, sep = ",")) %>% select(-new_assessor) diff --git a/man/obs_freq.Rd b/man/obs_freq.Rd index ac1f77d5..39a17755 100644 --- a/man/obs_freq.Rd +++ b/man/obs_freq.Rd @@ -22,7 +22,7 @@ set.seed(1234) obs_freq <- sample(x = seq(from = 100L, to = 500L, by = 1L), size = nrow(potato_visual), replace = TRUE) # We also create a set of repeated indices, used to extend the matrix rows -repeated_indices <- unlist(map2(1:nrow(potato_visual), obs_freq, ~ rep(.x, each = .y))) +repeated_indices <- unlist(Map(function(x, y) rep(x, each = y), 1:nrow(potato_visual), obs_freq)) # The potato_repeated matrix consists of all rows repeated corresponding to # the number of assessors in the obs_freq vector. This is how a large dataset # would look like without using the obs_freq argument @@ -84,7 +84,7 @@ obs_freq <- sample(x = 1:4, size = length(unique(beach_preferences$assessor)), r # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times beach_pref_rep <- beach_preferences \%>\% - mutate(new_assessor = lapply(obs_freq[assessor], ~ 1:.x)) \%>\% + mutate(new_assessor = lapply(obs_freq[assessor], function(x) 1:x)) \%>\% unnest(cols = new_assessor) \%>\% mutate(assessor = paste(assessor, new_assessor, sep = ",")) \%>\% select(-new_assessor) From 2b03f6d707b76b1364d3a0c13c9709d48648c14d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 09:46:36 +0100 Subject: [PATCH 14/41] hope I soon have removed every piece of purrr --- tests/testthat/test_compute_mallows.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/testthat/test_compute_mallows.R b/tests/testthat/test_compute_mallows.R index d3a4d662..4489eb46 100644 --- a/tests/testthat/test_compute_mallows.R +++ b/tests/testthat/test_compute_mallows.R @@ -120,7 +120,7 @@ test_that("compute_mallows treats obs_freq properly",{ # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times beach_pref_rep <- beach_small %>% - mutate(new_assessor = lapply(obs_freq[assessor], ~ 1:.x)) %>% + mutate(new_assessor = lapply(obs_freq[assessor], function(x) 1:x)) %>% unnest(cols = new_assessor) %>% mutate(assessor = paste(assessor, new_assessor, sep = ",")) %>% select(-new_assessor) From 445636292f7085ec7e133693f77266bef1ed9d81 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Thu, 18 Nov 2021 09:50:09 +0100 Subject: [PATCH 15/41] updated docs --- man/figures/README-unnamed-chunk-5-1.png | Bin 39829 -> 0 bytes man/figures/README-unnamed-chunk-6-1.png | Bin 24336 -> 0 bytes 2 files changed, 0 insertions(+), 0 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zY35jnDwBZSXc5C*cp#0~!cz+ZxEP2(N!saP3qu-rsSrsSxPz~_E_~maf7UXl4q_9? z95LMC5;m+5(i53*(BzISJh00^H@VHQ=Pho$D-H>oRC%=G$X^?uaHCwkVFAOL^)?TY zWT!yuJVLI*<=E+!n~Vak4!{^mQPUdGMcag+-%j*GYGEkUacGJCKeGu@g0$ hq%{A0i`0l;5yIaeiOA;V&p~0-MtTQz%e2Xq{{br=(24*6 From f3cae83a171b8b3538594d3643a2a84ab0961fec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Fri, 19 Nov 2021 10:46:08 +0100 Subject: [PATCH 16/41] Untidyr (#154) * removed tidyr from sample_mallows * removed tidyr from assign_cluster * removed tidyr from predict_top_k and updated tests * replaced tidyr in plot_top_k * replaced one tidyr in compute_consensus * replaced tidyr in compute_consensus and updated tests * replaced tidyr in generate_constraints * replaced tidyr in test_compute_mallows * removed a whole lot of dependencies * removed tidyr from Imports * updated news --- DESCRIPTION | 4 -- NEWS.md | 4 +- R/assign_cluster.R | 14 ++-- R/compute_consensus.R | 17 ++++- R/generate_constraints.R | 19 ++--- R/plot_top_k.R | 12 ++-- R/predict_top_k.R | 14 ++-- R/sample_mallows.R | 14 ++-- inst/examples/compute_consensus_example.R | 8 +-- .../compute_mallows_mixtures_example.R | 13 ++-- inst/examples/label_switching_example.R | 23 +++---- inst/examples/obs_freq_example.R | 19 +++-- man/compute_consensus.Rd | 8 +-- man/compute_mallows_mixtures.Rd | 13 ++-- man/label_switching.Rd | 23 +++---- man/obs_freq.Rd | 19 +++-- man/plot_elbow.Rd | 13 ++-- tests/permfun.R | 14 ++++ tests/testthat.R | 1 + tests/testthat/test_compute_consensus.R | 69 ++++++++++++------- tests/testthat/test_compute_mallows.R | 19 +++-- tests/testthat/test_distance_function.R | 4 +- tests/testthat/test_mcmc_function.R | 3 +- tests/testthat/test_partition_function.R | 5 +- tests/testthat/test_plot_top_k.R | 18 ++--- 25 files changed, 224 insertions(+), 146 deletions(-) create mode 100644 tests/permfun.R diff --git a/DESCRIPTION b/DESCRIPTION index 7ccacc7d..b0323195 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -50,7 +50,6 @@ Imports: Rcpp (>= 1.0.0), dplyr (>= 1.0.1), sets (>= 1.0-18), relations (>= 0.6-8), - tidyr (>= 1.1.1), rlang (>= 0.3.1), PerMallows (>= 1.13), HDInterval (>= 0.2.0), @@ -60,9 +59,6 @@ Suggests: R.rsp, testthat (>= 2.0), label.switching (>= 1.7), - readr (>= 1.3.1), - stringr (>= 1.4.0), - gtools (>= 3.8.1), rmarkdown, covr, parallel (>= 3.5.1) diff --git a/NEWS.md b/NEWS.md index 2483b3c4..8041ff5a 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,6 +1,8 @@ # BayesMallows 1.0.4.9000 -* Removed purrr dependency from Imports. +* Removed a large number of dependencies by converting to base R code. This will +make the package easier to install across a range of systems, and less vulnerable +to changes in other packages. # BayesMallows 1.0.4 diff --git a/R/assign_cluster.R b/R/assign_cluster.R index a7aaa2ef..4198041e 100644 --- a/R/assign_cluster.R +++ b/R/assign_cluster.R @@ -54,12 +54,14 @@ assign_cluster <- function(model_fit, burnin = model_fit$burnin, soft = TRUE, ex df <- dplyr::rename(df, cluster = .data$value) if(expand){ - df <- tidyr::complete( - dplyr::group_by(df, .data$assessor), - cluster = unique(df$cluster), - fill = list(probability = 0) - ) - df <- dplyr::ungroup(df) + df <- do.call(rbind, lapply(split(df, f = df$assessor), function(dd){ + dd2 <- merge(dd, expand.grid(cluster = unique(df$cluster)), by = "cluster", + all = TRUE) + dd2$assessor <- unique(dd$assessor) + dd2$probability[is.na(dd2$probability)] <- 0 + dd2 + })) + } # Compute the MAP estimate per assessor diff --git a/R/compute_consensus.R b/R/compute_consensus.R index 6ae014c8..995471c9 100644 --- a/R/compute_consensus.R +++ b/R/compute_consensus.R @@ -173,8 +173,11 @@ find_cpc <- function(group_df){ # Store the total number of iterations after burnin n_samples <- length(unique(df$iteration)) - # Spread to get items along columns - df <- tidyr::pivot_wider(df, names_from = "item", values_from = "value") + # Reshape to get items along columns + df <- stats::reshape(as.data.frame(df), direction = "wide", + idvar = c("cluster", "iteration"), + timevar = "item") + names(df) <- gsub("^value\\.", "", names(df)) # Group by everything except iteration, and count the unique combinations df <- dplyr::group_by_at(df, .vars = dplyr::vars(-.data$iteration)) @@ -191,7 +194,15 @@ find_cpc <- function(group_df){ df <- dplyr::select(df, -.data$n_max, -.data$n) # Now collect one set of ranks per cluster - df <- tidyr::pivot_longer(df, cols = -c("cluster", "probability"), names_to = "item", values_to = "map_ranking") + df$id <- seq_len(nrow(df)) + df <- stats::reshape(as.data.frame(df), direction = "long", + varying = setdiff(names(df), c("cluster", "probability", "id")), + v.names = "map_ranking", + timevar = "item", + idvar = c("cluster", "probability", "id"), + times = setdiff(names(df), c("cluster", "probability", "id"))) + rownames(df) <- NULL + df$id <- NULL # Sort according to cluster and ranking df <- dplyr::arrange(df, .data$cluster, .data$map_ranking) diff --git a/R/generate_constraints.R b/R/generate_constraints.R index 264f1777..a905c098 100644 --- a/R/generate_constraints.R +++ b/R/generate_constraints.R @@ -29,7 +29,8 @@ generate_constraints <- function(preferences, n_items, cl = NULL){ # Turn the preferences dataframe into a list of dataframes, # one list element per assessor - constraints <- split(preferences[, c("bottom_item", "top_item"), drop = FALSE], preferences$assessor) + constraints <- split(preferences[, c("bottom_item", "top_item"), drop = FALSE], + preferences$assessor) if(is.null(cl)) { lapply(constraints, constraint_fun, n_items) } else { @@ -44,12 +45,12 @@ constraint_fun <- function(x, n_items){ constrained_items <- unique(c(x[["bottom_item"]], x[["top_item"]])) # Now we must complete the dataframe with the items that do not appear - items_above <- tidyr::complete(dplyr::select(x, .data$bottom_item, .data$top_item), - bottom_item = seq(from = 1, to = n_items, by = 1), - fill = list(top_item = NA_integer_)) + items_above <- merge(dplyr::select(x, .data$bottom_item, .data$top_item), + expand.grid(bottom_item = seq(from = 1, to = n_items, by = 1)), + by = "bottom_item", all = TRUE) # Split it into a list, with one element per bottom_item - items_above <- split(items_above, items_above[["bottom_item"]]) + items_above <- split(items_above, items_above$bottom_item) # For each item, find which items are ranked above it items_above <- lapply(items_above, function(x) { @@ -58,12 +59,12 @@ constraint_fun <- function(x, n_items){ }) # Now we must complete the dataframe with the items that do not appear - items_below <- tidyr::complete(dplyr::select(x, .data$bottom_item, .data$top_item), - top_item = seq(from = 1, to = n_items, by = 1), - fill = list(bottom_item = NA_integer_)) + items_below <- merge(dplyr::select(x, .data$bottom_item, .data$top_item), + expand.grid(top_item = seq(from = 1, to = n_items, by = 1)), + by = "top_item", all = TRUE) # Split it into a list, with one element per bottom_item - items_below <- split(items_below, items_below[["top_item"]]) + items_below <- split(items_below, items_below$top_item) # For each item, find which items are ranked above it items_below <- lapply(items_below, function(x) { diff --git a/R/plot_top_k.R b/R/plot_top_k.R index 8e0aa3ca..0c32d496 100644 --- a/R/plot_top_k.R +++ b/R/plot_top_k.R @@ -38,11 +38,13 @@ plot_top_k <- function(model_fit, burnin = model_fit$burnin, rho <- dplyr::summarise(rho, prob = dplyr::n()/n_samples, .groups = "drop") # Find the complete set of items per cluster - rho <- tidyr::complete( - dplyr::group_by(rho, .data$cluster), - item = model_fit$items, - fill = list(prob = 0)) - rho <- dplyr::ungroup(rho) + rho <- do.call(rbind, lapply(split(rho, f = rho$cluster), function(dd){ + dd2 <- merge(dd, expand.grid(item = unique(rho$item)), + by = "item", all = TRUE) + dd2$cluster[is.na(dd2$cluster)] <- unique(dd$cluster) + dd2$prob[is.na(dd2$prob)] <- 0 + dd2 + }))[, c("cluster", "item", "prob")] # Sort the items according to probability in Cluster 1 item_ordering <- compute_consensus(model_fit, type = "CP", burnin = burnin) diff --git a/R/predict_top_k.R b/R/predict_top_k.R index aa7901cf..5a8f1daa 100644 --- a/R/predict_top_k.R +++ b/R/predict_top_k.R @@ -39,14 +39,14 @@ predict_top_k <- function(model_fit, burnin = model_fit$burnin, rankings <- dplyr::group_by(rankings, .data$assessor, .data$item) rankings <- dplyr::summarise(rankings, prob = dplyr::n()/n_samples, .groups = "drop") - rankings <- tidyr::complete( - dplyr::group_by(rankings, .data$assessor), - item = model_fit$items, - fill = list(prob = 0) - ) + do.call(rbind, lapply(split(rankings, f = rankings$assessor), function(dd){ + dd2 <- merge(dd, expand.grid(item = unique(rankings$item)), + by = "item", all = TRUE) + dd2$assessor[is.na(dd2$assessor)] <- unique(dd$assessor) + dd2$prob[is.na(dd2$prob)] <- 0 + dd2 + }))[, c("assessor", "item", "prob")] - - return(rankings) } diff --git a/R/sample_mallows.R b/R/sample_mallows.R index 848822fc..7ae54070 100644 --- a/R/sample_mallows.R +++ b/R/sample_mallows.R @@ -116,16 +116,20 @@ sample_mallows <- function(rho0, alpha0, n_samples, ggplot2::ggtitle("Autocorrelation of Rank Values") colnames(samples) <- seq(from = 1, to = n_items, by = 1) - diagnostic <- dplyr::as_tibble(samples) + diagnostic <- as.data.frame(samples) diagnostic <- dplyr::mutate(diagnostic, iteration = dplyr::row_number()) - diagnostic <- tidyr::gather(diagnostic, key = "item", - value = "value", -.data$iteration) + diagnostic <- stats::reshape(diagnostic, direction = "long", + varying = setdiff(names(diagnostic), "iteration"), + v.names = "value", + timevar = "item", + times = setdiff(names(diagnostic), "iteration"), + idvar = "iteration", + ids = diagnostic$iteration) + diagnostic <- dplyr::filter(diagnostic, .data$item %in% items_to_plot) diagnostic <- dplyr::mutate(diagnostic, item = as.factor(as.integer(.data$item))) - - rho_plot <- ggplot2::ggplot(diagnostic, ggplot2::aes(x = .data$iteration, y = .data$value, color = .data$item)) + ggplot2::geom_line() + diff --git a/inst/examples/compute_consensus_example.R b/inst/examples/compute_consensus_example.R index 2b9f2e10..9e32d136 100644 --- a/inst/examples/compute_consensus_example.R +++ b/inst/examples/compute_consensus_example.R @@ -20,13 +20,13 @@ compute_consensus(model_fit, type = "MAP") # Keeping the burnin at 1000, we can compute the consensus ranking per cluster model_fit$burnin <- 1000 cp_consensus_df <- compute_consensus(model_fit, type = "CP") - # Using dplyr::select and tidyr::cumprob we can now make a table - # which shows the ranking in each cluster: + # We can now make a table which shows the ranking in each cluster: library(dplyr) - library(tidyr) cp_consensus_df %>% select(-cumprob) %>% - spread(key = cluster, value = item) + as.data.frame() %>% + stats::reshape(direction = "wide", idvar = "ranking", + timevar = "cluster", varying = list(sort(unique(.$cluster)))) } \dontrun{ diff --git a/inst/examples/compute_mallows_mixtures_example.R b/inst/examples/compute_mallows_mixtures_example.R index c5307446..5e0c7986 100644 --- a/inst/examples/compute_mallows_mixtures_example.R +++ b/inst/examples/compute_mallows_mixtures_example.R @@ -24,8 +24,7 @@ # Plot the posterior distributions of alpha per cluster plot(mixture_model) # Compute the posterior interval of alpha per cluster - compute_posterior_intervals(mixture_model, - parameter = "alpha") + compute_posterior_intervals(mixture_model, parameter = "alpha") # Plot the posterior distributions of cluster probabilities plot(mixture_model, parameter = "cluster_probs") # Plot the posterior probability of cluster assignment @@ -35,15 +34,19 @@ # Compute the cluster-wise CP consensus, and show one column per cluster cp <- compute_consensus(mixture_model, type = "CP") library(dplyr) - library(tidyr) cp %>% select(-cumprob) %>% - spread(key = cluster, value = item) + as.data.frame() %>% + stats::reshape(direction = "wide", idvar = "ranking", + timevar = "cluster", varying = list(as.character(unique(.$cluster)))) + # Compute the MAP consensus, and show one column per cluster map <- compute_consensus(mixture_model, type = "MAP") map %>% select(-probability) %>% - spread(key = cluster, value = item) + as.data.frame() %>% + stats::reshape(direction = "wide", idvar = "map_ranking", + timevar = "cluster", varying = list(as.character(unique(.$cluster)))) # RUNNING IN PARALLEL # Computing Mallows models with different number of mixtures in parallel leads to diff --git a/inst/examples/label_switching_example.R b/inst/examples/label_switching_example.R index f2c1aba8..4d8334ee 100644 --- a/inst/examples/label_switching_example.R +++ b/inst/examples/label_switching_example.R @@ -12,12 +12,12 @@ # the cluster probabilites system.time(m <- compute_mallows(rankings = sushi_rankings, n_clusters = 6, nmc = 2000, save_clus = TRUE, - save_ind_clus = FALSE)) + save_ind_clus = FALSE, verbose = TRUE)) # With this options, compute_mallows will save cluster_probs2.csv, # cluster_probs3.csv, ..., cluster_probs[nmc].csv. system.time(m <- compute_mallows(rankings = sushi_rankings, n_clusters = 6, nmc = 2000, save_clus = TRUE, - save_ind_clus = TRUE)) + save_ind_clus = TRUE, verbose = TRUE)) # Next, we check convergence of alpha assess_convergence(m) @@ -32,8 +32,9 @@ paste(sum(do.call(file.size, list(cluster_files))) * 1e-6, "MB") # Find the iteration each file corresponds to, by extracting its number - library(stringr) - iteration_number <- as.integer(str_extract(cluster_files, "[:digit:]+")) + iteration_number <- as.integer( + gsub("(^[a-zA-Z\\_\\.]*)([0-9]+)([a-zA-Z\\_\\.]+$)", "\\2", + scluster_files, perl = TRUE)) # Remove all files before burnin file.remove(cluster_files[iteration_number <= burnin]) # Update the vector of files, after the deletion @@ -41,21 +42,19 @@ # Create 3d array, with dimensions (iterations, assessors, clusters) prob_array <- array(dim = c(length(cluster_files), m$n_assessors, m$n_clusters)) # Read each file, adding to the right element of the array - library(readr) for(i in seq_along(cluster_files)){ prob_array[i, , ] <- as.matrix( - read_delim(cluster_files[[i]], delim = ",", - col_names = FALSE, col_types = paste(rep("d", m$n_clusters), - collapse = ""))) + read.csv(cluster_files[[i]], header = FALSE)) } library(dplyr) - library(tidyr) - # Create an tnteger array of latent allocations, as this is required by label.switching + # Create an integer array of latent allocations, as this is required by label.switching z <- m$cluster_assignment %>% filter(iteration > burnin) %>% - mutate(value = as.integer(str_extract(value, "[:digit:]+"))) %>% - spread(key = assessor, value = value, sep = "_") %>% + mutate(value = as.integer(gsub("Cluster ", "", value))) %>% + as.data.frame() %>% + stats::reshape(direction = "wide", + idvar = "iteration", timevar = "assessor") %>% select(-iteration) %>% as.matrix() diff --git a/inst/examples/obs_freq_example.R b/inst/examples/obs_freq_example.R index 08b0c902..5d97e553 100644 --- a/inst/examples/obs_freq_example.R +++ b/inst/examples/obs_freq_example.R @@ -1,5 +1,4 @@ library(dplyr) -library(tidyr) # The first example uses full rankings in the potato_visual dataset, but we assume # that each row in the data corresponds to between 100 and 500 assessors. @@ -69,11 +68,19 @@ obs_freq <- sample(x = 1:4, size = length(unique(beach_preferences$assessor)), r # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times -beach_pref_rep <- beach_preferences %>% - mutate(new_assessor = lapply(obs_freq[assessor], function(x) 1:x)) %>% - unnest(cols = new_assessor) %>% - mutate(assessor = paste(assessor, new_assessor, sep = ",")) %>% - select(-new_assessor) +beach_pref_rep <- do.call( + rbind, + lapply(split(beach_preferences, f = beach_preferences$assessor), + function(dd){ + ret <- merge( + dd, + data.frame( + new_assessor = seq_len(obs_freq[unique(dd$assessor)]) + ), all = TRUE) + ret$assessor <- paste(ret$assessor, ret$new_assessor, sep = ",") + ret$new_assessor <- NULL + ret + })) # We generate transitive closure for these preferences beach_tc_rep <- generate_transitive_closure(beach_pref_rep) diff --git a/man/compute_consensus.Rd b/man/compute_consensus.Rd index a665dad4..8e7a8a78 100644 --- a/man/compute_consensus.Rd +++ b/man/compute_consensus.Rd @@ -59,13 +59,13 @@ compute_consensus(model_fit, type = "MAP") # Keeping the burnin at 1000, we can compute the consensus ranking per cluster model_fit$burnin <- 1000 cp_consensus_df <- compute_consensus(model_fit, type = "CP") - # Using dplyr::select and tidyr::cumprob we can now make a table - # which shows the ranking in each cluster: + # We can now make a table which shows the ranking in each cluster: library(dplyr) - library(tidyr) cp_consensus_df \%>\% select(-cumprob) \%>\% - spread(key = cluster, value = item) + as.data.frame() \%>\% + stats::reshape(direction = "wide", idvar = "ranking", + timevar = "cluster", varying = list(sort(unique(.$cluster)))) } \dontrun{ diff --git a/man/compute_mallows_mixtures.Rd b/man/compute_mallows_mixtures.Rd index 33ec592c..d29c4014 100644 --- a/man/compute_mallows_mixtures.Rd +++ b/man/compute_mallows_mixtures.Rd @@ -52,8 +52,7 @@ mixtures to use in the final model. # Plot the posterior distributions of alpha per cluster plot(mixture_model) # Compute the posterior interval of alpha per cluster - compute_posterior_intervals(mixture_model, - parameter = "alpha") + compute_posterior_intervals(mixture_model, parameter = "alpha") # Plot the posterior distributions of cluster probabilities plot(mixture_model, parameter = "cluster_probs") # Plot the posterior probability of cluster assignment @@ -63,15 +62,19 @@ mixtures to use in the final model. # Compute the cluster-wise CP consensus, and show one column per cluster cp <- compute_consensus(mixture_model, type = "CP") library(dplyr) - library(tidyr) cp \%>\% select(-cumprob) \%>\% - spread(key = cluster, value = item) + as.data.frame() \%>\% + stats::reshape(direction = "wide", idvar = "ranking", + timevar = "cluster", varying = list(as.character(unique(.$cluster)))) + # Compute the MAP consensus, and show one column per cluster map <- compute_consensus(mixture_model, type = "MAP") map \%>\% select(-probability) \%>\% - spread(key = cluster, value = item) + as.data.frame() \%>\% + stats::reshape(direction = "wide", idvar = "map_ranking", + timevar = "cluster", varying = list(as.character(unique(.$cluster)))) # RUNNING IN PARALLEL # Computing Mallows models with different number of mixtures in parallel leads to diff --git a/man/label_switching.Rd b/man/label_switching.Rd index 9da4e601..32d03304 100644 --- a/man/label_switching.Rd +++ b/man/label_switching.Rd @@ -32,12 +32,12 @@ determine the directory and filenames of the csv files. # the cluster probabilites system.time(m <- compute_mallows(rankings = sushi_rankings, n_clusters = 6, nmc = 2000, save_clus = TRUE, - save_ind_clus = FALSE)) + save_ind_clus = FALSE, verbose = TRUE)) # With this options, compute_mallows will save cluster_probs2.csv, # cluster_probs3.csv, ..., cluster_probs[nmc].csv. system.time(m <- compute_mallows(rankings = sushi_rankings, n_clusters = 6, nmc = 2000, save_clus = TRUE, - save_ind_clus = TRUE)) + save_ind_clus = TRUE, verbose = TRUE)) # Next, we check convergence of alpha assess_convergence(m) @@ -52,8 +52,9 @@ determine the directory and filenames of the csv files. paste(sum(do.call(file.size, list(cluster_files))) * 1e-6, "MB") # Find the iteration each file corresponds to, by extracting its number - library(stringr) - iteration_number <- as.integer(str_extract(cluster_files, "[:digit:]+")) + iteration_number <- as.integer( + gsub("(^[a-zA-Z\\\\_\\\\.]*)([0-9]+)([a-zA-Z\\\\_\\\\.]+$)", "\\\\2", + scluster_files, perl = TRUE)) # Remove all files before burnin file.remove(cluster_files[iteration_number <= burnin]) # Update the vector of files, after the deletion @@ -61,21 +62,19 @@ determine the directory and filenames of the csv files. # Create 3d array, with dimensions (iterations, assessors, clusters) prob_array <- array(dim = c(length(cluster_files), m$n_assessors, m$n_clusters)) # Read each file, adding to the right element of the array - library(readr) for(i in seq_along(cluster_files)){ prob_array[i, , ] <- as.matrix( - read_delim(cluster_files[[i]], delim = ",", - col_names = FALSE, col_types = paste(rep("d", m$n_clusters), - collapse = ""))) + read.csv(cluster_files[[i]], header = FALSE)) } library(dplyr) - library(tidyr) - # Create an tnteger array of latent allocations, as this is required by label.switching + # Create an integer array of latent allocations, as this is required by label.switching z <- m$cluster_assignment \%>\% filter(iteration > burnin) \%>\% - mutate(value = as.integer(str_extract(value, "[:digit:]+"))) \%>\% - spread(key = assessor, value = value, sep = "_") \%>\% + mutate(value = as.integer(gsub("Cluster ", "", value))) \%>\% + as.data.frame() \%>\% + stats::reshape(direction = "wide", + idvar = "iteration", timevar = "assessor") \%>\% select(-iteration) \%>\% as.matrix() diff --git a/man/obs_freq.Rd b/man/obs_freq.Rd index 39a17755..6a14d8c8 100644 --- a/man/obs_freq.Rd +++ b/man/obs_freq.Rd @@ -13,7 +13,6 @@ the observation frequencies. } \examples{ library(dplyr) -library(tidyr) # The first example uses full rankings in the potato_visual dataset, but we assume # that each row in the data corresponds to between 100 and 500 assessors. @@ -83,11 +82,19 @@ obs_freq <- sample(x = 1:4, size = length(unique(beach_preferences$assessor)), r # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times -beach_pref_rep <- beach_preferences \%>\% - mutate(new_assessor = lapply(obs_freq[assessor], function(x) 1:x)) \%>\% - unnest(cols = new_assessor) \%>\% - mutate(assessor = paste(assessor, new_assessor, sep = ",")) \%>\% - select(-new_assessor) +beach_pref_rep <- do.call( + rbind, + lapply(split(beach_preferences, f = beach_preferences$assessor), + function(dd){ + ret <- merge( + dd, + data.frame( + new_assessor = seq_len(obs_freq[unique(dd$assessor)]) + ), all = TRUE) + ret$assessor <- paste(ret$assessor, ret$new_assessor, sep = ",") + ret$new_assessor <- NULL + ret + })) # We generate transitive closure for these preferences beach_tc_rep <- generate_transitive_closure(beach_pref_rep) diff --git a/man/plot_elbow.Rd b/man/plot_elbow.Rd index 1d5dfaf5..359d9684 100644 --- a/man/plot_elbow.Rd +++ b/man/plot_elbow.Rd @@ -53,8 +53,7 @@ selecting the number of mixture. # Plot the posterior distributions of alpha per cluster plot(mixture_model) # Compute the posterior interval of alpha per cluster - compute_posterior_intervals(mixture_model, - parameter = "alpha") + compute_posterior_intervals(mixture_model, parameter = "alpha") # Plot the posterior distributions of cluster probabilities plot(mixture_model, parameter = "cluster_probs") # Plot the posterior probability of cluster assignment @@ -64,15 +63,19 @@ selecting the number of mixture. # Compute the cluster-wise CP consensus, and show one column per cluster cp <- compute_consensus(mixture_model, type = "CP") library(dplyr) - library(tidyr) cp \%>\% select(-cumprob) \%>\% - spread(key = cluster, value = item) + as.data.frame() \%>\% + stats::reshape(direction = "wide", idvar = "ranking", + timevar = "cluster", varying = list(as.character(unique(.$cluster)))) + # Compute the MAP consensus, and show one column per cluster map <- compute_consensus(mixture_model, type = "MAP") map \%>\% select(-probability) \%>\% - spread(key = cluster, value = item) + as.data.frame() \%>\% + stats::reshape(direction = "wide", idvar = "map_ranking", + timevar = "cluster", varying = list(as.character(unique(.$cluster)))) # RUNNING IN PARALLEL # Computing Mallows models with different number of mixtures in parallel leads to diff --git a/tests/permfun.R b/tests/permfun.R new file mode 100644 index 00000000..ee9b2c0f --- /dev/null +++ b/tests/permfun.R @@ -0,0 +1,14 @@ +## Source: https://stackoverflow.com/questions/11095992/generating-all-distinct-permutations-of-a-list-in-r +permutations <- function(n){ + if(n==1){ + return(matrix(1)) + } else { + sp <- permutations(n-1) + p <- nrow(sp) + A <- matrix(nrow=n*p,ncol=n) + for(i in 1:n){ + A[(i-1)*p+1:p,] <- cbind(i,sp+(sp>=i)) + } + return(A) + } +} diff --git a/tests/testthat.R b/tests/testthat.R index 06eb8dc1..c8594a2a 100644 --- a/tests/testthat.R +++ b/tests/testthat.R @@ -1,3 +1,4 @@ library(testthat) library(BayesMallows) test_check("BayesMallows") + diff --git a/tests/testthat/test_compute_consensus.R b/tests/testthat/test_compute_consensus.R index 34834a90..d79ea32c 100644 --- a/tests/testthat/test_compute_consensus.R +++ b/tests/testthat/test_compute_consensus.R @@ -44,20 +44,27 @@ test_that("compute_consensus computes correctly for rho", { 0.0510204081632653, 0.0510204081632653, 0.0510204081632653, 0.0510204081632653, 0.0510204081632653, 0.0510204081632653), item = c("Item 1", "Item 3", "Item 3", "Item 3", "Item 3", "Item 11", "Item 11", "Item 11", - "Item 11", "Item 1", "Item 12", "Item 7", "Item 10", "Item 7", - "Item 1", "Item 12", "Item 7", "Item 12", "Item 7", "Item 15", - "Item 12", "Item 15", "Item 14", "Item 1", "Item 15", "Item 14", - "Item 15", "Item 14", "Item 9", "Item 10", "Item 4", "Item 10", - "Item 13", "Item 4", "Item 10", "Item 4", "Item 14", "Item 6", - "Item 13", "Item 6", "Item 4", "Item 13", "Item 8", "Item 13", - "Item 6", "Item 9", "Item 6", "Item 9", "Item 5", "Item 8", "Item 5", - "Item 8", "Item 2", "Item 5", "Item 9", "Item 5", "Item 8", "Item 2", - "Item 2", "Item 2"), map_ranking = c(1, 1, 1, 1, 2, 2, 2, 2, + "Item 1", "Item 7", "Item 11", "Item 12", "Item 1", "Item 7", + "Item 10", "Item 12", "Item 7", "Item 7", "Item 12", "Item 15", + "Item 1", "Item 12", "Item 14", "Item 15", "Item 14", "Item 14", + "Item 15", "Item 15", "Item 4", "Item 9", "Item 10", "Item 10", + "Item 4", "Item 4", "Item 10", "Item 13", "Item 6", "Item 6", + "Item 13", "Item 14", "Item 4", "Item 8", "Item 13", "Item 13", + "Item 6", "Item 6", "Item 9", "Item 9", "Item 5", "Item 5", "Item 8", + "Item 8", "Item 2", "Item 5", "Item 5", "Item 9", "Item 2", "Item 2", + "Item 2", "Item 8"), map_ranking = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 12, 12, 12, 12, 13, 13, 13, 13, 14, 14, 14, 14, 15, 15, 15, 15)), row.names = c(NA, - -60L), class = c("tbl_df", "tbl", "data.frame")) - ) + -60L), reshapeLong = list(varying = structure(list(map_ranking = c("Item 1", + "Item 2", "Item 3", "Item 4", "Item 5", "Item 6", "Item 7", "Item 8", + "Item 9", "Item 10", "Item 11", "Item 12", "Item 13", "Item 14", + "Item 15")), v.names = "map_ranking", times = c("Item 1", "Item 2", + "Item 3", "Item 4", "Item 5", "Item 6", "Item 7", "Item 8", "Item 9", + "Item 10", "Item 11", "Item 12", "Item 13", "Item 14", "Item 15" + )), v.names = "map_ranking", idvar = c("cluster", "probability", + "id"), timevar = "item"), class = "data.frame") + ) }) @@ -111,33 +118,45 @@ test_that("compute_consensus computes augmented ranks correctly", { res <- compute_consensus(b2, type = "MAP", burnin = 200, parameter = "Rtilde", assessors = 1L) expect_equal(res, structure(list(probability = c(0.62, 0.62, 0.62), item = c("Item 1", "Item 3", "Item 2"), map_ranking = c(1, 2, 3)), row.names = c(NA, - -3L), class = c("tbl_df", "tbl", "data.frame")) - ) + -3L), reshapeLong = list(varying = structure(list(map_ranking = c("Item 1", + "Item 2", "Item 3")), v.names = "map_ranking", times = c("Item 1", + "Item 2", "Item 3")), v.names = "map_ranking", idvar = c("cluster", + "probability", "id"), timevar = "item"), class = "data.frame")) res <- compute_consensus(b3, type = "MAP", burnin = 200, parameter = "Rtilde", assessors = 1L) expect_equal(res, structure(list(probability = c(0.61, 0.61, 0.61), item = c("Item 1", "Item 3", "Item 2"), map_ranking = c(1, 2, 3)), row.names = c(NA, - -3L), class = c("tbl_df", "tbl", "data.frame")) - ) + -3L), reshapeLong = list(varying = structure(list(map_ranking = c("Item 1", + "Item 2", "Item 3")), v.names = "map_ranking", times = c("Item 1", + "Item 2", "Item 3")), v.names = "map_ranking", idvar = c("cluster", + "probability", "id"), timevar = "item"), class = "data.frame") + ) + res <- compute_consensus(b2, type = "MAP", burnin = 200, parameter = "Rtilde", assessors = c(5L, 3L)) expect_equal(res, structure(list(assessor = c(3, 3, 3, 5, 5, 5), probability = c(0.533333333333333, 0.533333333333333, 0.533333333333333, 1, 1, 1), item = c("Item 1", "Item 3", "Item 2", "Item 1", "Item 3", "Item 2"), map_ranking = c(1, - 2, 3, 1, 2, 3)), row.names = c(NA, -6L), class = c("tbl_df", - "tbl", "data.frame")) - ) + 2, 3, 1, 2, 3)), row.names = c(NA, -6L), reshapeLong = list(varying = structure(list( + map_ranking = c("Item 1", "Item 2", "Item 3")), v.names = "map_ranking", times = c("Item 1", + "Item 2", "Item 3")), v.names = "map_ranking", idvar = c("cluster", + "probability", "id"), timevar = "item"), class = "data.frame") + ) res <- compute_consensus(b3, type = "MAP", burnin = 200, parameter = "Rtilde", assessors = c(5L, 3L)) - expect_equal(res, - structure(list(assessor = c(3, 3, 3, 5, 5, 5), probability = c(0.53, - 0.53, 0.53, 1, 1, 1), item = c("Item 1", "Item 3", "Item 2", - "Item 1", "Item 3", "Item 2"), map_ranking = c(1, 2, 3, 1, 2, - 3)), row.names = c(NA, -6L), class = c("tbl_df", "tbl", "data.frame" - )) - ) + expect_equal( + res, + structure(list(assessor = c(3, 3, 3, 5, 5, 5), probability = c(0.53, + 0.53, 0.53, 1, 1, 1), item = c("Item 1", "Item 3", "Item 2", + "Item 1", "Item 3", "Item 2"), map_ranking = c(1, 2, 3, 1, 2, + 3)), row.names = c(NA, -6L), reshapeLong = list(varying = structure(list( + map_ranking = c("Item 1", "Item 2", "Item 3")), v.names = "map_ranking", times = c("Item 1", + "Item 2", "Item 3")), v.names = "map_ranking", idvar = c("cluster", + "probability", "id"), timevar = "item"), class = "data.frame") + + ) diff --git a/tests/testthat/test_compute_mallows.R b/tests/testthat/test_compute_mallows.R index 4489eb46..1af50787 100644 --- a/tests/testthat/test_compute_mallows.R +++ b/tests/testthat/test_compute_mallows.R @@ -1,5 +1,5 @@ library(dplyr) -library(tidyr) + context("Testing compute_mallows") @@ -119,11 +119,18 @@ test_that("compute_mallows treats obs_freq properly",{ # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times - beach_pref_rep <- beach_small %>% - mutate(new_assessor = lapply(obs_freq[assessor], function(x) 1:x)) %>% - unnest(cols = new_assessor) %>% - mutate(assessor = paste(assessor, new_assessor, sep = ",")) %>% - select(-new_assessor) + + beach_pref_rep <- do.call(rbind, lapply(split(beach_small, f = seq_len(nrow(beach_small))), function(dd){ + ret <- merge( + dd, + data.frame(new_assessor = seq_len(obs_freq[dd$assessor])), + all = TRUE + ) + ret$assessor <- paste(ret$assessor, ret$new_assessor, sep = ",") + ret$new_assessor <- NULL + ret + })) + # We generate transitive closure for these preferences beach_tc_rep <- generate_transitive_closure(beach_pref_rep) diff --git a/tests/testthat/test_distance_function.R b/tests/testthat/test_distance_function.R index 2df7fe6c..4a63481d 100644 --- a/tests/testthat/test_distance_function.R +++ b/tests/testthat/test_distance_function.R @@ -1,9 +1,9 @@ context("Testing computation of distance") - +source("../permfun.R") # Brute force formula check_dist <- function(n, fun){ # Generate all permutations - perm <- gtools::permutations(n, n) + perm <- permutations(n) apply(perm, 1, fun, r2 = 1:n) } diff --git a/tests/testthat/test_mcmc_function.R b/tests/testthat/test_mcmc_function.R index 6a6969b2..0d21112a 100644 --- a/tests/testthat/test_mcmc_function.R +++ b/tests/testthat/test_mcmc_function.R @@ -1,7 +1,7 @@ context("Testing MCMC function on potato data") library(dplyr) -library(tidyr) + # Testing with footrule set.seed(200) @@ -38,3 +38,4 @@ test_that( nrow() == 0 ) ) + diff --git a/tests/testthat/test_partition_function.R b/tests/testthat/test_partition_function.R index 74708e1b..4817d2d1 100644 --- a/tests/testthat/test_partition_function.R +++ b/tests/testthat/test_partition_function.R @@ -1,9 +1,12 @@ context("Testing computation of partition functions") +source("../permfun.R") + # Brute force formula check_log_zn <- function(n, alpha, metric){ # Generate all permutations - perm <- gtools::permutations(n, n) + perm <- permutations(n) + # Compute the partition function if(metric == "footrule") { diff --git a/tests/testthat/test_plot_top_k.R b/tests/testthat/test_plot_top_k.R index 0976d451..6babe3bf 100644 --- a/tests/testthat/test_plot_top_k.R +++ b/tests/testthat/test_plot_top_k.R @@ -42,24 +42,18 @@ test_that("plot_top_k and predict_top_k fail when they should", { expect_equal( pred[13:16, ], structure(list(assessor = c(3, 3, 3, 4), item = c("Item 3", "Item 4", - "Item 5", "Item 1"), prob = c(1, 0.3125, 0, 0.5625)), class = c("grouped_df", - "tbl_df", "tbl", "data.frame"), row.names = c(NA, -4L), groups = structure(list( - assessor = c(3, 4), .rows = structure(list(1:3, 4L), ptype = integer(0), class = c("vctrs_list_of", - "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame" - ), row.names = c(NA, -2L), .drop = TRUE)) - ) + "Item 5", "Item 1"), prob = c(1, 0.3125, 0, 0.5625)), row.names = c("3.3", + "3.4", "3.5", "4.1"), class = "data.frame") + ) pred <- predict_top_k(bmm, burnin = 4, k = 5) expect_equal( head(pred), structure(list(assessor = c(1, 1, 1, 1, 1, 2), item = c("Item 1", "Item 2", "Item 3", "Item 4", "Item 5", "Item 1"), prob = c(1, - 1, 1, 1, 1, 1)), class = c("grouped_df", "tbl_df", "tbl", "data.frame" - ), row.names = c(NA, -6L), groups = structure(list(assessor = c(1, - 2), .rows = structure(list(1:5, 6L), ptype = integer(0), class = c("vctrs_list_of", - "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame" - ), row.names = c(NA, -2L), .drop = TRUE)) - ) + 1, 1, 1, 1, 1)), row.names = c("1.1", "1.2", "1.3", "1.4", "1.5", + "2.1"), class = "data.frame") + ) }) From 98b263ec1b3a45e587c3c9e57e4f35ea58406649 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Fri, 19 Nov 2021 11:10:11 +0100 Subject: [PATCH 17/41] Untidyr (#156) * removed tidyr from sample_mallows * removed tidyr from assign_cluster * removed tidyr from predict_top_k and updated tests * replaced tidyr in plot_top_k * replaced one tidyr in compute_consensus * replaced tidyr in compute_consensus and updated tests * replaced tidyr in generate_constraints * replaced tidyr in test_compute_mallows * removed a whole lot of dependencies * removed tidyr from Imports * updated news * moved permutations function to R/misc.R --- R/misc.R | 16 ++++++++++++++++ tests/testthat/test_partition_function.R | 1 - 2 files changed, 16 insertions(+), 1 deletion(-) diff --git a/R/misc.R b/R/misc.R index ca085f07..213f5988 100644 --- a/R/misc.R +++ b/R/misc.R @@ -106,3 +106,19 @@ fill_single_entries <- function (data) { } return(data) } + + +## Source: https://stackoverflow.com/questions/11095992/generating-all-distinct-permutations-of-a-list-in-r +permutations <- function(n){ + if(n==1){ + return(matrix(1)) + } else { + sp <- permutations(n-1) + p <- nrow(sp) + A <- matrix(nrow=n*p,ncol=n) + for(i in 1:n){ + A[(i-1)*p+1:p,] <- cbind(i,sp+(sp>=i)) + } + return(A) + } +} diff --git a/tests/testthat/test_partition_function.R b/tests/testthat/test_partition_function.R index 4817d2d1..55459687 100644 --- a/tests/testthat/test_partition_function.R +++ b/tests/testthat/test_partition_function.R @@ -1,6 +1,5 @@ context("Testing computation of partition functions") -source("../permfun.R") # Brute force formula check_log_zn <- function(n, alpha, metric){ From 2514f11a879d00d30e9e7153c5897116e8e3b275 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Fri, 19 Nov 2021 11:34:26 +0100 Subject: [PATCH 18/41] Removed permfun.R and remaining source of it --- tests/permfun.R | 14 -------------- tests/testthat/test_distance_function.R | 1 - 2 files changed, 15 deletions(-) delete mode 100644 tests/permfun.R diff --git a/tests/permfun.R b/tests/permfun.R deleted file mode 100644 index ee9b2c0f..00000000 --- a/tests/permfun.R +++ /dev/null @@ -1,14 +0,0 @@ -## Source: https://stackoverflow.com/questions/11095992/generating-all-distinct-permutations-of-a-list-in-r -permutations <- function(n){ - if(n==1){ - return(matrix(1)) - } else { - sp <- permutations(n-1) - p <- nrow(sp) - A <- matrix(nrow=n*p,ncol=n) - for(i in 1:n){ - A[(i-1)*p+1:p,] <- cbind(i,sp+(sp>=i)) - } - return(A) - } -} diff --git a/tests/testthat/test_distance_function.R b/tests/testthat/test_distance_function.R index 4a63481d..031f7097 100644 --- a/tests/testthat/test_distance_function.R +++ b/tests/testthat/test_distance_function.R @@ -1,5 +1,4 @@ context("Testing computation of distance") -source("../permfun.R") # Brute force formula check_dist <- function(n, fun){ # Generate all permutations From e241d5fc5c32b9dcbce54e274dc8e3dd51871b30 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 22 Nov 2021 13:54:47 +0100 Subject: [PATCH 19/41] SMC-Mallows (#157) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Identified more codelines related to #90 * Reclassified Rcpp objects as arma (#90) In the smc_leap_and_shift_probs() function. * Increment version number * Fixed unit tests * Updated TODOs * Increment version number * Replaced Rcpp::sample with arma::randi (#90) * Adjusted unit tests * Replaced Rcpp vector with arma vector (#90) * Removed TODO For the moment, smc_mallows_new_users_complete() will still use Rcpp::sample() to calculate the arma::uvec index. Replacement of Rcpp::sample() with arma::rand*() is encouraged, but left to any future developer aware of how to include probability weights on arma::rand*(). For more details, see conversation in issue #90. * Rearranged inline comments Top comment was relocated and rewritten on https://github.com/ocbe-uio/BayesMallows/issues/90#issuecomment-866614296 for better visibility and organization. Mid-code comment added for future reference. * Syntax fix * Reduced example length (closes #96) * Increment version number * Reduced unit test parameters (resolves #100) * Dropped usage of deprecated function (closes #99) * Added original scripts (#106) * Fitting scripts to package format (#106) * Added original scripts * Removed source calls, moved unit tests * Syntax fixes * Updated docs, exported new functions * Added placeholders for unit tests * Commented out failing tests These tests trigger errors on get_rank_distance(), to be investigated later (see issue #98 comments section for more details). * Increment version number * Increment version number * Syntax fix aug_prob is a vector, so we need to select just one element to perform those operations. * Fixed function name Function was using a deprecated, alternative name. * Fixed calls to get_mallows_loglik (#98, #109) * Reduced number of iterations on test * Restyled file * Fixes tests (#109) * Implemented tests (#98) * Edited test titles * Removed TODO * Improved SMC tests' speed (fixes #102) * Moved get_sample_probabilities() to src folder * Translated get_sample_probabilities + docs (#98) * Moved calculate_forward_probability to src folder * Exporting empty C++ function * Partial translation of calculate_forward_probability * Added get_sample_probabilities to smc.h * Finished translation of calculate_forward_probability Also: - Updated build version + Rcpp exports - Increment version number * Updated unit test expectations * Removed unnecessary library call * Translated calculate_backward_probability to C++ * Increment version number * Syntax fixes + refactoring * Changed file Unix permissions to match other files Used chmod to change the file permissions from 755 to 644 so it matches the rest of the files on the folder. * Added files for SMC-Mallows new item rank * Updated SMC partial rankings functions and tests * Restyled files * Syntax fixes * Commented out code to be worked on (#110) * Increment version number * Syntax fix * Formatted unit tests (#110) * Reduced test time (#110) Further test optimization Check fixes * Improvements to validation * Added SMC dataset * Improved partial ranking tests (#110) * Improved API feedback * Adapted test units for new item rank (#110) * Optimized unit tests (#110) * Minor fixes for clean check * Translated metropolis_hastings_aug_ranking_pseudo (#98) - Added skeleton of C++ metropolis_hastings_aug_ranking_pseudo - Translated metropolis_hastings_aug_ranking_pseudo - Replaced R version with C++ version - Fixed behavior of metropolis_hastings_aug_ranking_pseudo - Improved test format and updated expectations * Increment version number and R version dependency * Refactoring for easier debugging * Fixed RNG behavior on unit test * Removed superseded function * Added translation of metropolis_hastings_aug_ranking * Fixed documentation The docs for metropolis_hastings_aug_ranking_CPP() had text from the pseudo function. * Replaced R version of mh_aug_ranking with C++ (#98) Also, adjusted unit test expectations accordingly * Increment version number * Adjusted tests and renamed arguments (#98) * Added original code for correction_kernel C++ (#111) * Syntax fixes * Increment version number * Replaced R with C++ version of correction_kernel (#111) * Refactoring * Code optimization * Renamed cpp file * Added original code for correction_kernel_pseudo C++ (#111) * Syntax fixes * Replaced R with C++ of correction_kernel_pseudo (#111) * Increment version number * Replaced calculation of remaining_set * Translated smc_mallows_new_users_partial (#107) - Added C++ skeleton of smc_mallows_new_users_partial - Translated function * Set more seeds in unit tests This avoids random errors in unit testing. * Increment version number * Fixed calculation on smc_mallows_new_users_complete * Replaced R version with C++ version (#107) Of smc_mallows_new_users_partial(). * cal_fwd_prob() now works with both R and C++ indices * Refactoring * Adjusting unit tests * Returning test units to their original expectation * Updated Roxygen version * cal_bwd_prob() now works with both R and C++ indices This is basically a copy-paste from commit ce5d52cd. I smell a function coming... * Refactoring * Adjusting test expectations to new code output The differing output is caused by `set.seed(101)` having a different effect on the new C++ functions, particularly the output of `test_1_forward$aug_ranking`, which trickles down into the calculation of `test_1_backward_b`, thus causing different probabilities. The code was back-checked with the original R code, in which case the output seems the same. However, this should be checked with Anja. * Moved common code into function As "promised" on e497763640992f788c4ff0c98f6f127b99d4d785. * Translated partial ranks alpha fixed to C++ (#107) - Added skeleton of C++ alpha_fixed function - Finished translation - Switched R and C++ versions - Updated unit test expectations - Removed the R version of the function * Added parameters to SMC new users complete Added alpha_prob_sd, lambda and alpha_max, which were new arguments to metropolis_hastings_alpha and were internally defined as constants. * Added knitr as vignette engine Now both R.rsp and knitr are used as vignette builders. The former is used to add the BM paper in PDF format, the latter is hosting the SMC Rmarkdown vignette (yet a mockup). * Added mockup of SMC vignette * Updated .gitignore Update generated by `devtools::build_vignettes()`. * Updated build version * Translated new_item_rank to C++ (#113) * Replaced R new_item_rank with C++ version (#113) * Increment version number * Opening CI to all branches r.yml is currently configured for only triggering GitHub Actions CI workflow for the `master` and the `main` (inexistent here) branches. Opening it up to any branch so commits to `smc-mallows` also get triggered. This is important so we can better foresee problems in the upcoming merge with `master`. Opening up to any branch name shouldn't be a problem on this repository, since `master` is the only long-lived branch at the moment. * Syntax fixes * Refactoring * More specific seed setting * Fixing tests for Ubuntu Tests for Mac and Windows are still failing due to RNG. To be addressed later. * Investigate tests (#127) * Added prints statements to `arma::shuffle` * Added unique sort to `setdiff` * Updated tests Co-authored-by: Øystein Sørensen * Updates to smc_post_processing_functions.R (#119) * Updates to smc_post_processing_functions.R Hi, Just done a bit of tidying with the post_processing_functions. Here is a summary of my proposed changes: 1. edited `smc_processing` to allow the option to print the `col_names` of the original dataset if appropriate 2. edited `compute_consensus` to print the output correctly in the `MAP` case and to print the `colnames` of a dataset if appropriate instead of listing items as 'item 1, item 2, ... etc'. 3. edited `plot_posterior_intervals_rho` to print the `colnames` of a dataset if appropriate instead of listing items as 'item 1, item 2, ... etc'. 4. adjusted the font sizes of the axis labels in `plot_heatplot_rho` 5. removed the variable `verbose` from `plot_posterior_alpha` so that it now prints when calling the functions and doesn't do duplicate plots. 6. commented out any unused post-processing functions I have added some general comments, which begin with `#AS:` to explain why I have made the following changes (even though Github will highlight them). Let me know if you disagree or think any changes are unnecessary. I am planning on renaming the variables in the functions `heatMat` and `heatplot_fixed` so that they are consistent with the other heatplot functions, but I shall do this once this pull request has been completed/resolved(?). Waldir's changes: * Changed test unit expectations * Documentation fixes * Removed commented-out code * Replaced dplyr call with base R Co-authored-by: Waldir Leoncio * Updated smc.rmd Copy-pasted vignette script to github. Need to upload the matching .bib file containing the references. * create vignette.bib This file contains the references to be printed in the smc.Rmd file * Remove unused columns The final columns of rho_samples and alpha_samples do not get populated with non-zero values, so they are redundant. The same change applies to smc_mallows_new_item_rank_alpha_fixed, so this will appear as another pull request. * Remove unused column Extra column in rho_samples is redundant * Update compute_posterior_intervals_rho Reordered column in output to print item names in correct numerical order "item 1, item 2, ..., item 10" instead of "item 1, item 10, item 2, ..., item 9" * Cosmetic changes to heatplot functions Renamed variables to keep variable names consistent. Added some comments to explain the steps in the functions. * Updated test expectation Test units should be reset to expect one fewer columns, given the most recent code changes proposed. * Updated test expectations The previous changes on this branch reduce the number of columns by 1, so this must be reflected in the relevant unit test expectations. * Syntax fixes + doc update * Syntax fixes * Increment version number * Syntax adjustments * Changed test expectations back to what they should be See comment https://github.com/ocbe-uio/BayesMallows/issues/116#issuecomment-938583180 for more details. * Trying to reproduce Anja's seed and output * Increment version number * Added C++ version of smc_mallows_new_item_rank_alpha_fixed (#113) * Replaced R with C++ of smc_mallows_new_item_rank_alpha_fixed (#113) * Added C++ function of SMC new item alpha fixed (#113) * Added C++ version of smc_mallows_new_item_rank_alpha_fixed (#113) * Replaced R with C++ of smc_mallows_new_item_rank_alpha_fixed (#113) * Added C++ function of SMC new item alpha fixed (#113) * Added unit tests for #134 Co-authored-by: Waldir Leoncio Co-authored-by: Anja Stein * Reduced test time * get_mallows_loglik now transposes as needed (#134) This eliminated the bug when passing rankings as a row vector and rho as a column vector (or vice-versa). Both still need to be matrices, though. * Increment version Increment version number to 1.0.3.9002 Increment version number to 1.0.3.9003 Increment version number to 1.0.3.9004 * Updated build version * Exported all functions from #117 - Exported `plot_rho_heatplot()` - Exported `plot_alpha_posterior()` - Exported `compute_posterior_intervals_alpha()` - Exported `compute_posterior_intervals_rho()` - Exported `compute_rho_consensus()` Also, some function names were renamed in comments and the SMC vignette text due to slight differences between their names there and the one on the code. The names on the code were preferred. Namely, the changes were: 1. `plot_heatplot_rho` into `plot_rho_heatplot` 2. `compute_consensus_rho` into `compute_rho_consensus` * Refactoring * Increment version number to 1.0.3.9006 * Added print argument to internal function * Updated test expectation, added comments * Added temporary print statements for debugging * Increment version number to 1.0.3.9007 * Added function plot_rho_posterior.R in smc_post_processing_functions.R (#137) * Added function plot_rho_posterior.R in smc_post_processing_functions.R * Synxtax fixes + whitespace removal * Exporting function with documentation Co-authored-by: Waldir Leoncio * Changing output of SMC function to new class Including change in test expectation. * Added generics, methods for compute_posterior_intervals * Changed classes of Mallows objects (#80) Generics and methods related to `compute_posterior_intervals()` had some class additions to allow for better dispatching between BayesMallows and SMC-Mallows methods. * Moved methods to same file (#80) Moved all methods of `compute_posterior_intervals()` and `.compute_posterior_intervals()` to the same file (easier to manage). * Syntax and documentation fixes/updates * Increment version number to 1.0.3.9006 * Updated TODOs * Update smc.Rmd Removed the heatplot function and replaced it with the plot rho posterior function. Some descriptions have changed to fit the description of the posterior function. * Remove heatplot functions Since we now have the `plot_rho_posterior` function to clearer visualisation, we can discontinue this function. * Deleted commented-out code * Updated docs * Removed unit test for dropped function * Increment version number to 1.0.3.9007 * Moved gtools from Suggests to Depends Also, standardized indentation. * Updated build version * added print statements to calculate_forward_probability.cpp Added lots of print statements to help with debugging * added print statements to calculate_backwards_probability.cpp added extra print statements for debugging * Fixed bug causing issue #116 Co-authored-by: Waldir Leoncio Co-authored-by: Anja Stein * Removed temporary code * Updated test expectations * Removed gtools dependency on SMC (#142) Removed dependency on gtools::mixedorder Works by extracting everything that isn't a digit ([0-9]) and ultimately using the index of the sorted elements to sort `rho_posterior_interval`. Works well for the standard "Item DD" format that SMC uses, and probably for other formats as well, but this wasn't extensively tested. * Replaced purrr::map with lapply on SMC (#142) * Replaced tidyr calls in SMC functions (#142) - Replaced tidyr::gather with stats::reshape - Replaced tidyr::spreadh with stats::reshape * Minor adjustments to SMC vignette Removed unused dependencies, Removed usage of , automatic whitespace adjustments. * Renamed SMC-Mallows vignette file So it matches the capitalization of "BayesMallows.pdf.asis" * Removed unneeded suggestions from DESCRIPTION * pedantic point * I suggest using a single file for references, to avoid duplication of data. * fixed some typos and tried to make the vignette code more consistent with the rest of the package, include <- for assignment * changed assignment operator in vignette * renamed all testfiles from test_ to test- * updated testthat version number in DESCRIPTION * ran styler on vignette * Created test which catches the warning in #159 * Fixed the warning, closes #159 * Mentions SMC in DESCRIPTION. * slight update in DESCRIPTION * updated news.md and incremented version number Co-authored-by: Anja Stein Co-authored-by: Anja Stein Co-authored-by: Øystein Sørensen Co-authored-by: Øystein Sørensen Co-authored-by: Anja Stein Co-authored-by: Anja Stein --- .gitignore | 2 + DESCRIPTION | 38 +- NAMESPACE | 27 + NEWS.md | 7 +- R/RcppExports.R | 461 +++++++++++++++++- R/compute_posterior_intervals.R | 158 +++++- R/data.R | 18 + R/misc_likelihood.R | 4 +- R/smc_post_processing_functions.R | 421 ++++++++++++++++ data/data_10_6_smc.rda | Bin 0 -> 497 bytes inst/REFERENCES.bib | 73 +++ .../metropolis_hastings_alpha_example.R | 37 ++ .../smc_mallows_new_users_complete_example.R | 22 + man/calculate_backward_probability.Rd | 45 ++ man/calculate_forward_probability.Rd | 49 ++ man/compute_consensus_smc.Rd | 26 + ...ompute_posterior_intervals.BayesMallows.Rd | 41 ++ man/compute_posterior_intervals.Rd | 22 +- man/compute_posterior_intervals.SMCMallows.Rd | 38 ++ man/compute_posterior_intervals_alpha.Rd | 26 + man/compute_posterior_intervals_rho.Rd | 34 ++ man/compute_rho_consensus.Rd | 40 ++ man/correction_kernel.Rd | 28 ++ man/correction_kernel_pseudo.Rd | 39 ++ man/get_mallows_loglik.Rd | 60 +++ man/get_sample_probabilities.Rd | 36 ++ man/leap_and_shift_probs.Rd | 38 ++ man/metropolis_hastings_alpha.Rd | 103 ++++ man/metropolis_hastings_aug_ranking.Rd | 37 ++ man/metropolis_hastings_aug_ranking_pseudo.Rd | 38 ++ man/metropolis_hastings_rho.Rd | 65 +++ man/plot_alpha_posterior.Rd | 23 + man/plot_rho_posterior.Rd | 27 + man/sample_dataset.Rd | 41 ++ man/smc_mallows_new_item_rank.Rd | 64 +++ man/smc_mallows_new_item_rank_alpha_fixed.Rd | 72 +++ man/smc_mallows_new_users_complete.Rd | 96 ++++ man/smc_mallows_new_users_partial.Rd | 67 +++ ...c_mallows_new_users_partial_alpha_fixed.Rd | 55 +++ man/smc_processing.Rd | 19 + src/RcppExports.cpp | 305 ++++++++++++ src/distances.cpp | 1 - src/misc.cpp | 20 + src/misc.h | 1 + src/missing_data.cpp | 2 +- src/smc.h | 18 + src/smc_calculate_backward_probability.cpp | 86 ++++ src/smc_calculate_forward_probability.cpp | 107 ++++ src/smc_correction_kernel.cpp | 66 +++ src/smc_correction_kernel_pseudo.cpp | 123 +++++ src/smc_get_mallows_loglik.cpp | 72 +++ src/smc_get_sample_probabilities.cpp | 46 ++ src/smc_leap_and_shift_probs.cpp | 97 ++++ src/smc_mallows_new_item_rank.cpp | 356 ++++++++++++++ src/smc_mallows_new_item_rank_alpha_fixed.cpp | 341 +++++++++++++ src/smc_mallows_new_users_complete.cpp | 200 ++++++++ src/smc_mallows_new_users_partial.cpp | 244 +++++++++ ..._mallows_new_users_partial_alpha_fixed.cpp | 224 +++++++++ src/smc_metropolis_hastings_alpha.cpp | 78 +++ src/smc_metropolis_hastings_aug_ranking.cpp | 82 ++++ ...metropolis_hastings_aug_ranking_pseudo.cpp | 93 ++++ src/smc_metropolis_hastings_rho.cpp | 73 +++ ...onvergence.R => test-assess_convergence.R} | 0 tests/testthat/test-bulletproofing.R | 104 ++++ ...e_consensus.R => test-compute_consensus.R} | 0 ...mpute_mallows.R => test-compute_mallows.R} | 0 ...ce_function.R => test-distance_function.R} | 0 ...n.R => test-estimate_partition_function.R} | 0 ...t_expected_dist.R => test-expected_dist.R} | 0 ...rate_ranking.R => test-generate_ranking.R} | 0 .../{test_lik_db_mix.R => test-lik_db_mix.R} | 0 ...t_mcmc_function.R => test-mcmc_function.R} | 0 .../{test_misc_cpp.R => test-misc_cpp.R} | 0 ...misc_functions.R => test-misc_functions.R} | 0 ...n_function.R => test-partition_function.R} | 0 tests/testthat/{test_plot.R => test-plot.R} | 0 .../{test_plot_elbow.R => test-plot_elbow.R} | 0 .../{test_plot_top_k.R => test-plot_top_k.R} | 0 tests/testthat/test-print.R | 29 +- ...umber_seed.R => test-random_number_seed.R} | 0 ...nk_conversion.R => test-rank_conversion.R} | 0 ...nk_freq_distr.R => test-rank_freq_distr.R} | 0 ...sample_mallows.R => test-sample_mallows.R} | 0 .../testthat/test-smc_individual_functions.R | 208 ++++++++ .../test-smc_mallows_complete_rankings.R | 180 +++++++ .../testthat/test-smc_mallows_new_item_rank.R | 109 +++++ .../test-smc_mallows_partial_rankings.R | 267 ++++++++++ tests/testthat/test-smc_pseudolikelihood.R | 144 ++++++ tests/testthat/test-smc_uniform.R | 85 ++++ ...ve_closure.R => test-transitive_closure.R} | 0 tests/testthat/test_print.R | 27 - vignettes/SMC-Mallows.Rmd | 369 ++++++++++++++ 92 files changed, 6563 insertions(+), 91 deletions(-) create mode 100644 R/smc_post_processing_functions.R create mode 100644 data/data_10_6_smc.rda create mode 100644 inst/examples/metropolis_hastings_alpha_example.R create mode 100644 inst/examples/smc_mallows_new_users_complete_example.R create mode 100644 man/calculate_backward_probability.Rd create mode 100644 man/calculate_forward_probability.Rd create mode 100644 man/compute_consensus_smc.Rd create mode 100644 man/compute_posterior_intervals.BayesMallows.Rd create mode 100644 man/compute_posterior_intervals.SMCMallows.Rd create mode 100644 man/compute_posterior_intervals_alpha.Rd create mode 100644 man/compute_posterior_intervals_rho.Rd create mode 100644 man/compute_rho_consensus.Rd create mode 100644 man/correction_kernel.Rd create mode 100644 man/correction_kernel_pseudo.Rd create mode 100644 man/get_mallows_loglik.Rd create mode 100644 man/get_sample_probabilities.Rd create mode 100644 man/leap_and_shift_probs.Rd create mode 100644 man/metropolis_hastings_alpha.Rd create mode 100644 man/metropolis_hastings_aug_ranking.Rd create mode 100644 man/metropolis_hastings_aug_ranking_pseudo.Rd create mode 100644 man/metropolis_hastings_rho.Rd create mode 100644 man/plot_alpha_posterior.Rd create mode 100644 man/plot_rho_posterior.Rd create mode 100644 man/sample_dataset.Rd create mode 100644 man/smc_mallows_new_item_rank.Rd create mode 100644 man/smc_mallows_new_item_rank_alpha_fixed.Rd create mode 100644 man/smc_mallows_new_users_complete.Rd create mode 100644 man/smc_mallows_new_users_partial.Rd create mode 100644 man/smc_mallows_new_users_partial_alpha_fixed.Rd create mode 100644 man/smc_processing.Rd create mode 100644 src/smc.h create mode 100644 src/smc_calculate_backward_probability.cpp create mode 100644 src/smc_calculate_forward_probability.cpp create mode 100644 src/smc_correction_kernel.cpp create mode 100644 src/smc_correction_kernel_pseudo.cpp create mode 100644 src/smc_get_mallows_loglik.cpp create mode 100644 src/smc_get_sample_probabilities.cpp create mode 100644 src/smc_leap_and_shift_probs.cpp create mode 100644 src/smc_mallows_new_item_rank.cpp create mode 100644 src/smc_mallows_new_item_rank_alpha_fixed.cpp create mode 100644 src/smc_mallows_new_users_complete.cpp create mode 100644 src/smc_mallows_new_users_partial.cpp create mode 100644 src/smc_mallows_new_users_partial_alpha_fixed.cpp create mode 100644 src/smc_metropolis_hastings_alpha.cpp create mode 100644 src/smc_metropolis_hastings_aug_ranking.cpp create mode 100644 src/smc_metropolis_hastings_aug_ranking_pseudo.cpp create mode 100644 src/smc_metropolis_hastings_rho.cpp rename tests/testthat/{test_assess_convergence.R => test-assess_convergence.R} (100%) create mode 100644 tests/testthat/test-bulletproofing.R rename tests/testthat/{test_compute_consensus.R => test-compute_consensus.R} (100%) rename tests/testthat/{test_compute_mallows.R => test-compute_mallows.R} (100%) rename tests/testthat/{test_distance_function.R => test-distance_function.R} (100%) rename tests/testthat/{test_estimate_partition_function.R => test-estimate_partition_function.R} (100%) rename tests/testthat/{test_expected_dist.R => test-expected_dist.R} (100%) rename tests/testthat/{test_generate_ranking.R => test-generate_ranking.R} (100%) rename tests/testthat/{test_lik_db_mix.R => test-lik_db_mix.R} (100%) rename tests/testthat/{test_mcmc_function.R => test-mcmc_function.R} (100%) rename tests/testthat/{test_misc_cpp.R => test-misc_cpp.R} (100%) rename tests/testthat/{test_misc_functions.R => test-misc_functions.R} (100%) rename tests/testthat/{test_partition_function.R => test-partition_function.R} (100%) rename tests/testthat/{test_plot.R => test-plot.R} (100%) rename tests/testthat/{test_plot_elbow.R => test-plot_elbow.R} (100%) rename tests/testthat/{test_plot_top_k.R => test-plot_top_k.R} (100%) rename tests/testthat/{test_random_number_seed.R => test-random_number_seed.R} (100%) rename tests/testthat/{test_rank_conversion.R => test-rank_conversion.R} (100%) rename tests/testthat/{test_rank_freq_distr.R => test-rank_freq_distr.R} (100%) rename tests/testthat/{test_sample_mallows.R => test-sample_mallows.R} (100%) create mode 100644 tests/testthat/test-smc_individual_functions.R create mode 100755 tests/testthat/test-smc_mallows_complete_rankings.R create mode 100644 tests/testthat/test-smc_mallows_new_item_rank.R create mode 100644 tests/testthat/test-smc_mallows_partial_rankings.R create mode 100644 tests/testthat/test-smc_pseudolikelihood.R create mode 100644 tests/testthat/test-smc_uniform.R rename tests/testthat/{test_transitive_closure.R => test-transitive_closure.R} (100%) delete mode 100644 tests/testthat/test_print.R create mode 100644 vignettes/SMC-Mallows.Rmd diff --git a/.gitignore b/.gitignore index fe1ec1da..85c85770 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,5 @@ inst/doc work-docs/ doc Meta +/doc/ +/Meta/ diff --git a/DESCRIPTION b/DESCRIPTION index b0323195..efd0b612 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: BayesMallows Type: Package Title: Bayesian Preference Learning with the Mallows Rank Model -Version: 1.0.4.9000 +Version: 1.0.4.9001 Authors@R: c(person("Oystein", "Sorensen", email = "oystein.sorensen.1985@gmail.com", role = c("aut", "cre"), @@ -27,22 +27,23 @@ Authors@R: c(person("Oystein", "Sorensen", email = "w.l.netto@medisin.uio.no", role = c("ctr"))) Maintainer: Oystein Sorensen -Description: An implementation of the Bayesian version of the Mallows rank model - (Vitelli et al., Journal of Machine Learning Research, 2018 ; - Crispino et al., Annals of Applied Statistics, 2019 ). Both Cayley, footrule, - Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be - analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well - as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the - posterior distributions of parameters are provided. The package also provides functions for estimating - the partition function (normalizing constant) of the Mallows rank model, both with the importance - sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm +Description: An implementation of the Bayesian version of the Mallows rank model + (Vitelli et al., Journal of Machine Learning Research, 2018 ; + Crispino et al., Annals of Applied Statistics, 2019 ). Both Metropolis-Hastings + and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule, + Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be + analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well + as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the + posterior distributions of parameters are provided. The package also provides functions for estimating + the partition function (normalizing constant) of the Mallows rank model, both with the importance + sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm (Mukherjee, Annals of Statistics, 2016 ). URL: https://github.com/ocbe-uio/BayesMallows License: GPL-3 Encoding: UTF-8 LazyData: true RoxygenNote: 7.1.2 -Depends: R (>= 2.10) +Depends: R (>= 3.5.0) Imports: Rcpp (>= 1.0.0), ggplot2 (>= 3.1.0), Rdpack (>= 1.0), @@ -56,11 +57,12 @@ Imports: Rcpp (>= 1.0.0), cowplot (>= 1.0.0) LinkingTo: Rcpp, RcppArmadillo Suggests: - R.rsp, - testthat (>= 2.0), - label.switching (>= 1.7), - rmarkdown, - covr, - parallel (>= 3.5.1) -VignetteBuilder: R.rsp + R.rsp, + knitr, + testthat (>= 3.0.0), + label.switching (>= 1.7), + rmarkdown, + covr, + parallel (>= 3.5.1) +VignetteBuilder: R.rsp, knitr RdMacros: Rdpack diff --git a/NAMESPACE b/NAMESPACE index dc5b247a..9588503f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,14 +1,21 @@ # Generated by roxygen2: do not edit by hand +S3method(compute_posterior_intervals,BayesMallows) +S3method(compute_posterior_intervals,SMCMallows) S3method(plot,BayesMallows) S3method(print,BayesMallows) S3method(print,BayesMallowsMixtures) export(assess_convergence) export(assign_cluster) +export(calculate_backward_probability) +export(calculate_forward_probability) export(compute_consensus) export(compute_mallows) export(compute_mallows_mixtures) export(compute_posterior_intervals) +export(compute_posterior_intervals_alpha) +export(compute_posterior_intervals_rho) +export(compute_rho_consensus) export(create_ordering) export(create_ranking) export(estimate_partition_function) @@ -16,14 +23,34 @@ export(expected_dist) export(generate_constraints) export(generate_initial_ranking) export(generate_transitive_closure) +export(get_mallows_loglik) +export(get_sample_probabilities) +export(leap_and_shift_probs) export(lik_db_mix) +export(metropolis_hastings_alpha) +export(metropolis_hastings_aug_ranking) +export(metropolis_hastings_aug_ranking_pseudo) +export(metropolis_hastings_rho) +export(plot_alpha_posterior) export(plot_elbow) +export(plot_rho_posterior) export(plot_top_k) export(predict_top_k) export(rank_distance) export(rank_freq_distr) export(sample_mallows) +export(smc_mallows_new_item_rank) +export(smc_mallows_new_item_rank_alpha_fixed) +export(smc_mallows_new_users_complete) +export(smc_mallows_new_users_partial) +export(smc_mallows_new_users_partial_alpha_fixed) importFrom(Rcpp,sourceCpp) importFrom(Rdpack,reprompt) +importFrom(graphics,mtext) +importFrom(graphics,par) importFrom(rlang,.data) +importFrom(stats,dexp) +importFrom(stats,rexp) +importFrom(stats,rlnorm) +importFrom(stats,runif) useDynLib(BayesMallows, .registration = TRUE) diff --git a/NEWS.md b/NEWS.md index 8041ff5a..fbdb85fd 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,9 @@ +# BayesMallows 1.0.4.9001 + +* This is a major update, with new functions for estimating the Bayesian Mallows + model using sequential Monte Carlo. The methods are described in the vignette + titled "SMC-Mallows Tutorial". + # BayesMallows 1.0.4.9000 * Removed a large number of dependencies by converting to base R code. This will @@ -24,7 +30,6 @@ to changes in other packages. * Fixed critical bug which caused results to be wrong with more than one mixture component in compute_mallows() and compute_mallows_mixtures(). Thanks to Anja Stein for discovering the bug. - # BayesMallows 1.0.2 * Function generate_initial_ranking() now has two additional options for generating random initial rankings. This can help with convergence problems, by allowing the MCMC algorithm to run from a range of different starting points. diff --git a/R/RcppExports.R b/R/RcppExports.R index 90f50f39..8f5f0f2f 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -19,7 +19,6 @@ #' #' @references \insertAllCited{} #' @keywords internal -#' get_rank_distance <- function(r1, r2, metric) { .Call(`_BayesMallows_get_rank_distance`, r1, r2, metric) } @@ -191,3 +190,463 @@ run_mcmc <- function(rankings, obs_freq, nmc, constraints, cardinalities, logz_e .Call(`_BayesMallows_run_mcmc`, rankings, obs_freq, nmc, constraints, cardinalities, logz_estimate, rho_init, metric, error_model, Lswap, n_clusters, include_wcd, leap_size, alpha_prop_sd, alpha_init, alpha_jump, lambda, alpha_max, psi, rho_thinning, aug_thinning, clus_thin, save_aug, verbose, kappa_1, kappa_2, save_ind_clus) } +#' @title Calculate Backward Probability +#' @description Function to calculate probability of assigning a set of specific ranks to an specific item +#' given its rank in the consensus ranking +#' +#' @param item_ordering A vector of integer values to represent the specified queue of which unranked item to assign a rank for the proposed augmented ranking +#' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs +#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +#' @param remaining_set A vector of integer values to represent the elements (ranks) missing from original observed ranking +#' @param rho Numeric vector specifying the consensus ranking +#' @param alpha Numeric value og the scale parameter +#' @param n_items Integer is the number of items in a ranking +#' @param metric A character string specifying the distance metric to use in the +#' Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @return backward_auxiliary_ranking_probability A numerical value of creating the previous augmented ranking using the same item ordering used to create the +#' new auggmented ranking in calculate_forward_probability funtion. +#' @export +calculate_backward_probability <- function(item_ordering, partial_ranking, current_ranking, remaining_set, rho, alpha, n_items, metric) { + .Call(`_BayesMallows_calculate_backward_probability`, item_ordering, partial_ranking, current_ranking, remaining_set, rho, alpha, n_items, metric) +} + +#' @title Calculate Forward Probability +#' @description Function to calculate probability of assigning a set of +#' specific ranks to an specific item +#' given its rank in the consensus ranking +#' @export +#' +#' @param item_ordering A vector of integer values to represent the specified +#' queue of which unranked item to assign a rank for the proposed augmented +#' ranking +#' @param partial_ranking An incomplete rank sequence vector of the original +#' observed incomplete ranking which contains NAs +#' @param remaining_set A vector of integer values to represent the elements +#' (ranks) missing from original observed ranking +#' @param rho Numeric vector specifying the consensus ranking +#' @param alpha Numeric value og the scale parameter +#' @param n_items Integer is the number of items in a ranking +#' @param metric A character string specifying the distance metric to use in +#' the Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, +#' and \code{"ulam"}. +#' @return List containing aug_ranking, a ranking sequence vector of the +#' proposed augmented ranking and forward_prob a numerical value of the +#' probability of creating the augmented ranking using the pseudolikelihood +#' augmentation. +calculate_forward_probability <- function(item_ordering, partial_ranking, remaining_set, rho, alpha, n_items, metric) { + .Call(`_BayesMallows_calculate_forward_probability`, item_ordering, partial_ranking, remaining_set, rho, alpha, n_items, metric) +} + +#' @title Correction Kernel +#' @description Function to determine if the augmented ranking is compatible +#' with the new observed partial ranking. If it is not, the we create a new +#' augmentation using the random sampling approachand calculate the +#' augmentation probability. +#' +#' @param current_ranking A ranking sequence vector of the current augmented +#' ranking (no missing values) +#' @param observed_ranking A ranking sequence vector of the observed partial +#' ranking (no missing values) The original incomplete partial ranking +#' is in the rankings data set. +#' @param n_items Integer is the number of items in a ranking +#' +#' @return List containing the proposed 'corrected' augmented ranking +#' that is compatible with the new observed ranking for a user +correction_kernel <- function(observed_ranking, current_ranking, n_items) { + .Call(`_BayesMallows_correction_kernel`, observed_ranking, current_ranking, n_items) +} + +#' @title Correction Kernel (pseudolikelihood) +#' @description Function to determine if the augmented ranking is compatible with the new observed partial ranking. +#' If it is not, the we create a new augmentation using the pseudolikelihood approach and calculate the augmentation probability. +#' +#' @param observed_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs +#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +#' @param rho Numeric vector specifying the consensus ranking +#' @param alpha Numeric value og the scale parameter +#' @param n_items Integer is the number of items in a ranking +#' @param metric A character string specifying the distance metric to use in the +#' Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @return list containing R_obs, the proposed 'corrected' augmented ranking that is compatible with the new observed ranking for a user, and +#' forward_auxiliary_ranking_probability, a numerical value for the probability of correcting the ranking to be compatible with R_obs. +correction_kernel_pseudo <- function(current_ranking, observed_ranking, rho, alpha, n_items, metric) { + .Call(`_BayesMallows_correction_kernel_pseudo`, current_ranking, observed_ranking, rho, alpha, n_items, metric) +} + +#' @title Get Mallows log-likelihood +#' @description Calculates the Mallows log-likelihood given a set of rankings and a given rank sequence +#' @param alpha Numeric value of the scale parameter +#' @param rho A ranking sequence +#' @param n_items Integer is the number of items in a ranking +#' A matrix of size \eqn{N }\eqn{\times}{x}\eqn{ n_items} of +#' rankings in each row. Alternatively, if \eqn{N} equals 1, \code{rankings} +#' can be a vector. +#' @param rankings A matrix of size \eqn{N }\eqn{\times}{x}\eqn{ n_items} of +#' rankings in each row. Alternatively, if \eqn{N} equals 1, \code{rankings} +#' can be a vector. +#' @param metric Character string specifying the distance measure to use. +#' Available options are \code{"kendall"}, \code{"cayley"}, \code{"hamming"}, +#' \code{"ulam"}, \code{"footrule"} and \code{"spearman"}. +#' @return Mallows log-likelihood +#' @export +#' @author Anja Stein +#' @examples +#' set.seed(101) +#' rho <- t(c(1, 2, 3, 4, 5, 6)) +#' alpha <- 2 +#' metric <- "footrule" +#' n_items <- 6 +#' get_mallows_loglik( +#' alpha = alpha, rho = rho, n_items = length(rho), rankings = rho, +#' metric = metric +#' ) +#' +#' # return 0 because you are comparing the consensus ranking with itself +#' # if you change alpha or metric, then the result shall remain as 0 +#' +#' rankings <- sample_mallows( +#' rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 +#' ) +#' +#' # depending on your seed, you will get a different collection of rankings in R and C++ +#' +#' get_mallows_loglik( +#' alpha = alpha, rho = rho, n_items = n_items, rankings = rankings , +#' metric = metric +#' ) +get_mallows_loglik <- function(alpha, rho, n_items, rankings, metric) { + .Call(`_BayesMallows_get_mallows_loglik`, alpha, rho, n_items, rankings, metric) +} + +#' @title Get Sample Probabilities +#' @description Calculate probability of assigning a set of specific ranks to an specific item +#' given its rank in the consensus ranking +#' +#' @param rho_item_rank An integer value rank of an item in the current consensus ranking +#' @param alpha Numeric value og the scale parameter +#' @param remaining_set_ranks A sequence of integer values of the set of possible ranks that we can assign the item +#' @param metric A character string specifying the distance metric to use in the +#' Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @param n_items Integer is the number of items in the consensus ranking +#' @return sample_prob_list A numeric sequence of sample probabilities for selecting a specific rank given the current +#' rho_item_rank +#' @export +get_sample_probabilities <- function(rho_item_rank, alpha, remaining_set_ranks, metric, n_items) { + .Call(`_BayesMallows_get_sample_probabilities`, rho_item_rank, alpha, remaining_set_ranks, metric, n_items) +} + +#' @title Leap and Shift Probabilities +#' @description Determine the new Calculates transition probabilities for proposing a new rho +#' @param rho A ranking sequence +#' @param leap_size Integer specifying the step size of the leap-and-shift +#' proposal distribution. +#' @param n_items Integer is the number of items in a ranking +#' @export +#' @return A list containing: +#' \itemize{ +#' \item \code{rho_prime} A ranking sequence proposed consensus ranking +#' \item \code{forwards_prob} Numeric value to account for transition probability from rho to rho_prime +#' \item \code{backwards_prob} Numeric Value to account for the transition probability from \code{rho_prime} to \code{rho} +#' } +#' +#' @examples +#' rho <- c(1, 2, 3, 4, 5, 6) +#' n_items <- 6 +#' +#' leap_and_shift_probs(rho, 1, n_items) +#' leap_and_shift_probs(rho, 2, n_items) +#' leap_and_shift_probs(rho, 3, n_items) +#' @author Anja Stein +#' +leap_and_shift_probs <- function(rho, leap_size, n_items) { + .Call(`_BayesMallows_leap_and_shift_probs`, rho, leap_size, n_items) +} + +#' @title SMC-Mallows new users rank +#' @description Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user +#' at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudlikelihood augmentation. +#' @param n_items Integer is the number of items in a ranking +#' @param R_obs 3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps +#' @param metric A character string specifying the distance metric to use in the +#' Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @param leap_size leap_size Integer specifying the step size of the leap-and-shift +#' proposal distribution +#' @param N Integer specifying the number of particles +#' @param Time Integer specifying the number of time steps in the SMC algorithm +#' @param logz_estimate Estimate of the partition function, computed with +#' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. +#' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +#' @param alpha_prop_sd Numeric value of the standard deviation of the prior distribution for alpha +#' @param lambda Strictly positive numeric value specifying the rate parameter +#' of the truncated exponential prior distribution of alpha. +#' @param alpha_max Maximum value of alpha in the truncated exponential +#' prior distribution. +#' @param aug_method A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random" +#' @param verbose Logical specifying whether to print out the progress of the +#' SMC-Mallows algorithm. Defaults to \code{FALSE}. +#' @return a 3d matrix containing the samples of rho and alpha from the SMC algorithm +#' @export +smc_mallows_new_item_rank <- function(n_items, R_obs, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, alpha_prop_sd, lambda, alpha_max, aug_method, verbose = FALSE) { + .Call(`_BayesMallows_smc_mallows_new_item_rank`, n_items, R_obs, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, alpha_prop_sd, lambda, alpha_max, aug_method, verbose) +} + +#' @title SMC-Mallows new item rank (alpha fixed) +#' @description Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user +#' at each time step. Each correction and augmentation is done by filling in the missing item ranks randomly. +#' @param alpha A numeric value of the true scale parameter +#' @param n_items Integer is the number of items in a ranking +#' @param R_obs 3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps +#' @param metric A character string specifying the distance metric to use in the +#' Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @param leap_size leap_size Integer specifying the step size of the leap-and-shift +#' proposal distribution +#' @param N Integer specifying the number of particles +#' @param Time Integer specifying the number of time steps in the SMC algorithm +#' @param logz_estimate Estimate of the partition function, computed with +#' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. +#' @param mcmc_kernel_app Integer value for the number of applications we apply the MCMC move kernel +#' @param alpha_prop_sd Numeric value specifying the standard deviation of the +#' lognormal proposal distribution used for \eqn{\alpha} in the +#' Metropolis-Hastings algorithm. Defaults to \code{0.1}. +#' @param lambda Strictly positive numeric value specifying the rate parameter +#' of the truncated exponential prior distribution of \eqn{\alpha}. Defaults +#' to \code{0.1}. When \code{n_cluster > 1}, each mixture component +#' \eqn{\alpha_{c}} has the same prior distribution. +#' @param alpha_max Maximum value of \code{alpha} in the truncated exponential +#' prior distribution. +#' @param aug_method A character string specifying the approach for filling in +#' the missing data, options are "pseudolikelihood" or "random". +#' @param verbose Logical specifying whether to print out the progress of the +#' SMC-Mallows algorithm. Defaults to \code{FALSE}. +#' @return a 3d matrix containing the samples of rho and alpha from the SMC algorithm +#' @export +smc_mallows_new_item_rank_alpha_fixed <- function(alpha, n_items, R_obs, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, alpha_prop_sd, lambda, alpha_max, aug_method, verbose = FALSE) { + .Call(`_BayesMallows_smc_mallows_new_item_rank_alpha_fixed`, alpha, n_items, R_obs, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, alpha_prop_sd, lambda, alpha_max, aug_method, verbose) +} + +#' @title SMC-Mallows New Users Complete +#' @description Function to perform resample-move SMC algorithm where we +#' receive new users with complete rankings at each time step +#' +#' @param R_obs Matrix containing the full set of observed rankings of size +#' n_assessors by n_items +#' @param n_items Integer is the number of items in a ranking +#' @param metric A character string specifying the distance metric to use +#' in the Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @param leap_size leap_size Integer specifying the step size of the +#' leap-and-shift proposal distribution +#' @param N Integer specifying the number of particles +#' @param Time Integer specifying the number of time steps in the SMC algorithm +#' @param logz_estimate Estimate of the partition function, computed with +#' \code{\link{estimate_partition_function}} in the BayesMallow R package +#' {estimate_partition_function}. +#' @param mcmc_kernel_app Interger value for the number of applications we +#' apply the MCMC move kernel +#' @param num_new_obs Integer value for the number of new observations +#' (complete rankings) for each time step +#' @param alpha_prop_sd Numeric value specifying the standard deviation of the +#' lognormal proposal distribution used for \eqn{\alpha} in the +#' Metropolis-Hastings algorithm. Defaults to \code{0.1}. +#' @param lambda Strictly positive numeric value specifying the rate parameter +#' of the truncated exponential prior distribution of \eqn{\alpha}. Defaults +#' to \code{0.1}. When \code{n_cluster > 1}, each mixture component +#' \eqn{\alpha_{c}} has the same prior distribution. +#' @param alpha_max Maximum value of \code{alpha} in the truncated exponential +#' prior distribution. +#' @param verbose Logical specifying whether to print out the progress of the +#' SMC-Mallows algorithm. Defaults to \code{FALSE}. +#' +#' @return a set of particles each containing a value of rho and alpha +#' +#' @importFrom stats rexp +#' @export +#' +#' @example inst/examples/smc_mallows_new_users_complete_example.R +#' +smc_mallows_new_users_complete <- function(R_obs, n_items, metric, leap_size, N, Time, mcmc_kernel_app, num_new_obs, alpha_prop_sd, lambda, alpha_max, logz_estimate = NULL, verbose = FALSE) { + .Call(`_BayesMallows_smc_mallows_new_users_complete`, R_obs, n_items, metric, leap_size, N, Time, mcmc_kernel_app, num_new_obs, alpha_prop_sd, lambda, alpha_max, logz_estimate, verbose) +} + +#' @title SMC-Mallows new users partial +#' @description Function to perform resample-move SMC algorithm where we receive new users with complete rankings +#' at each time step +#' @param R_obs Matrix containing the full set of observed rankings of size n_assessors by n_items +#' @param n_items Integer is the number of items in a ranking +#' @param metric A character string specifying the distance metric to use in the +#' Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @param leap_size leap_size Integer specifying the step size of the leap-and-shift +#' proposal distribution +#' @param N Integer specifying the number of particles +#' @param Time Integer specifying the number of time steps in the SMC algorithm +#' @param logz_estimate Estimate of the partition function, computed with +#' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. +#' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +#' @param num_new_obs Integer value for the number of new observations (complete rankings) for each time step +#' @param alpha_prop_sd Numeric value of the standard deviation of the prior distribution for alpha +#' @param lambda Strictly positive numeric value specifying the rate parameter +#' of the truncated exponential prior distribution of alpha. +#' @param alpha_max Maximum value of alpha in the truncated exponential +#' prior distribution. +#' @param aug_method A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random" +#' @param verbose Logical specifying whether to print out the progress of the +#' SMC-Mallows algorithm. Defaults to \code{FALSE}. +#' @return a set of particles each containing a value of rho and alpha +#' @export +smc_mallows_new_users_partial <- function(R_obs, n_items, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, num_new_obs, alpha_prop_sd, lambda, alpha_max, aug_method, verbose = FALSE) { + .Call(`_BayesMallows_smc_mallows_new_users_partial`, R_obs, n_items, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, num_new_obs, alpha_prop_sd, lambda, alpha_max, aug_method, verbose) +} + +#' @title SMC-mallows new users partial (alpha fixed) +#' @description Function to perform resample-move SMC algorithm where we receive new users with complete rankings +#' at each time step +#' @param R_obs Matrix containing the full set of observed rankings of size n_assessors by n_items +#' @param n_items Integer is the number of items in a ranking +#' @param metric A character string specifying the distance metric to use in the +#' Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @param leap_size leap_size Integer specifying the step size of the leap-and-shift +#' proposal distribution +#' @param N Integer specifying the number of particles +#' @param Time Integer specifying the number of time steps in the SMC algorithm +#' @param logz_estimate Estimate of the partition function, computed with +#' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. +#' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +#' @param num_new_obs Integer value for the number of new observations (complete rankings) for each time step +#' @param aug_method A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random" +#' @param alpha A numeric value of the scale parameter which is known and fixed +#' @return a set of particles each containing a value of rho and alpha +#' @export +smc_mallows_new_users_partial_alpha_fixed <- function(R_obs, n_items, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, num_new_obs, aug_method, alpha) { + .Call(`_BayesMallows_smc_mallows_new_users_partial_alpha_fixed`, R_obs, n_items, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, num_new_obs, aug_method, alpha) +} + +#' @title Metropolis-Hastings Alpha +#' @description Function to perform Metropolis-Hastings for new rho under +#' the Mallows model with footrule distance metric! +#' @param alpha Numeric value og the scale parameter +#' @param n_items Integer is the number of items in a ranking +#' @param rankings the observed rankings, i.e, preference data +#' @details \code{rankings} is a matrix of size +#' \eqn{N }\eqn{\times}{x}\eqn{ n_items} of rankings in each row. +#' Alternatively, if \eqn{N} equals 1, \code{rankings} can be a vector. +#' @param metric A character string specifying the distance metric to use +#' in the Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, +#' and \code{"ulam"}. +#' @param rho Numeric vector specifying the current consensus ranking +#' @param logz_estimate Estimate grid of log of partition function, +#' computed with \code{\link{estimate_partition_function}} in +#' the BayesMallow R package {estimate_partition_function}. +#' @param alpha_prop_sd Numeric value specifying the standard deviation of the +#' lognormal proposal distribution used for \eqn{\alpha} in the +#' Metropolis-Hastings algorithm. Defaults to \code{0.1}. +#' @return \code{alpha} or \code{alpha_prime}: Numeric value to be used +#' as the proposal of a new alpha +#' @param lambda Strictly positive numeric value specifying the rate parameter +#' of the truncated exponential prior distribution of \eqn{\alpha}. Defaults +#' to \code{0.1}. When \code{n_cluster > 1}, each mixture component +#' \eqn{\alpha_{c}} has the same prior distribution. +#' @param alpha_max Maximum value of \code{alpha} in the truncated exponential +#' prior distribution. +#' @importFrom stats dexp rlnorm runif +#' @author Anja Stein +#' @example /inst/examples/metropolis_hastings_alpha_example.R +#' +#' @export +metropolis_hastings_alpha <- function(alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd, lambda, alpha_max) { + .Call(`_BayesMallows_metropolis_hastings_alpha`, alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd, lambda, alpha_max) +} + +#' @title Metropolis-Hastings Augmented Ranking +#' @description Function to perform Metropolis-Hastings for new augmented ranking +#' +#' @param alpha Numeric value og the scale parameter +#' @param rho Numeric vector specifying the consensus ranking +#' @param n_items Integer is the number of items in a ranking +#' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs +#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +#' @param metric A character string specifying the distance metric to use in the +#' Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @return R_curr or R_obs A ranking sequence vector representing proposed augmented ranking for next iteration of MCMC chain +#' @export +metropolis_hastings_aug_ranking <- function(alpha, rho, n_items, partial_ranking, current_ranking, metric) { + .Call(`_BayesMallows_metropolis_hastings_aug_ranking`, alpha, rho, n_items, partial_ranking, current_ranking, metric) +} + +#' @title Metropolis-Hastings Augmented Ranking (pseudolikelihood) +#' @description Function to perform Metropolis-Hastings for new augmented ranking using the pseudolikelihood augmentation approach +#' +#' @param alpha Numeric value og the scale parameter +#' @param rho Numeric vector specifying the consensus ranking +#' @param n_items Integer is the number of items in a ranking +#' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs +#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +#' @param metric A character string specifying the distance metric to use in the +#' Bayesian Mallows Model. Available options are \code{"footrule"}, +#' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +#' \code{"ulam"}. +#' @return = proposed augmented ranking or current ranking A ranking sequence vector representing proposed augmented ranking for next +#' iteration of MCMC chain +#' @export +metropolis_hastings_aug_ranking_pseudo <- function(alpha, rho, n_items, partial_ranking, current_ranking, metric) { + .Call(`_BayesMallows_metropolis_hastings_aug_ranking_pseudo`, alpha, rho, n_items, partial_ranking, current_ranking, metric) +} + +#' @title Metropolis-Hastings Rho +#' @description Function to perform Metropolis-Hastings for new rho under the Mallows model with footrule distance metric! +#' @inheritParams get_mallows_loglik +#' @param leap_size Integer specifying the step size of the leap-and-shift +#' proposal distribution. +#' @export +#' @author Anja Stein +#' @examples +#' rho <- t(c(1,2,3,4,5,6)) +#' alpha <- 2 +#' metric <- "footrule" +#' n_items <- 6 +#' +#' metropolis_hastings_rho( +#' alpha = alpha, n_items = n_items, rankings = rho, metric = metric, +#' rho = rho, leap_size = 1 +#' ) +#' +#' metropolis_hastings_rho( +#' alpha = alpha, n_items = n_items, rankings = rho, metric = metric, +#' rho = rho, leap_size = 2 +#' ) +#' +#' metropolis_hastings_rho( +#' alpha = alpha, n_items = n_items, rankings = rho, metric = metric, +#' rho = rho, leap_size = 3 +#' ) +#' +#' rankings <- sample_mallows( +#' rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 +#' ) +#' metropolis_hastings_rho( +#' alpha = alpha, n_items = n_items, rankings = rankings, metric = metric, +#' rho = rho, leap_size = 1 +#' ) +#' +metropolis_hastings_rho <- function(alpha, n_items, rankings, metric, rho, leap_size) { + .Call(`_BayesMallows_metropolis_hastings_rho`, alpha, n_items, rankings, metric, rho, leap_size) +} + diff --git a/R/compute_posterior_intervals.R b/R/compute_posterior_intervals.R index 4b471e1a..3e31a13a 100644 --- a/R/compute_posterior_intervals.R +++ b/R/compute_posterior_intervals.R @@ -5,19 +5,7 @@ #' #' @param model_fit An object returned from \code{\link{compute_mallows}}. #' -#' @param burnin A numeric value specifying the number of iterations -#' to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be -#' provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}. -#' -#' @param parameter Character string defining which parameter to compute -#' posterior intervals for. One of \code{"alpha"}, \code{"rho"}, or -#' \code{"cluster_probs"}. Default is \code{"alpha"}. -#' -#' @param level Decimal number in \eqn{[0,1]} specifying the confidence level. -#' Defaults to \code{0.95}. -#' -#' @param decimals Integer specifying the number of decimals to include -#' in posterior intervals and the mean and median. Defaults to \code{3}. +#' @param ... other arguments passed to methods. #' #' @details This function computes both the Highest Posterior Density Interval (HPDI), #' which may be discontinuous for bimodal distributions, and @@ -33,9 +21,33 @@ #' #' @export #' -compute_posterior_intervals <- function(model_fit, burnin = model_fit$burnin, - parameter = "alpha", level = 0.95, - decimals = 3L){ +compute_posterior_intervals <- function(model_fit, ...) { + UseMethod("compute_posterior_intervals") +} + +.compute_posterior_intervals <- function(df, ...) { + UseMethod(".compute_posterior_intervals") +} + +#' @title Compute posterior intervals +#' @inheritParams compute_posterior_intervals +#' @param burnin A numeric value specifying the number of iterations +#' to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be +#' provided if \code{model_fit$burnin} does not exist. +#' See \code{\link{assess_convergence}}. +#' @param parameter Character string defining which parameter to compute +#' posterior intervals for. One of \code{"alpha"}, \code{"rho"}, or +#' \code{"cluster_probs"}. Default is \code{"alpha"}. +#' @param level Decimal number in \eqn{[0,1]} specifying the confidence level. +#' Defaults to \code{0.95}. +#' @param decimals Integer specifying the number of decimals to include +#' in posterior intervals and the mean and median. Defaults to \code{3}. +#' @seealso assess_convergence +#' @export +compute_posterior_intervals.BayesMallows <- function( + model_fit, burnin = model_fit$burnin, parameter = "alpha", level = 0.95, + decimals = 3L, ... +) { stopifnot(class(model_fit) == "BayesMallows") if(is.null(burnin)){ @@ -51,11 +63,13 @@ compute_posterior_intervals <- function(model_fit, burnin = model_fit$burnin, if(parameter == "alpha" || parameter == "cluster_probs"){ df <- dplyr::group_by(df, .data$cluster) + class(df) <- c("BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") df <- .compute_posterior_intervals(df, parameter, level, decimals) } else if(parameter == "rho"){ decimals <- 0 df <- dplyr::group_by(df, .data$cluster, .data$item) + class(df) <- c("BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") df <- .compute_posterior_intervals(df, parameter, level, decimals, discrete = TRUE) } @@ -67,8 +81,51 @@ compute_posterior_intervals <- function(model_fit, burnin = model_fit$burnin, return(df) } +#' @title Compute posterior intervals +#' @inheritParams compute_posterior_intervals.BayesMallows +#' @export +compute_posterior_intervals.SMCMallows <- function( + model_fit, burnin = model_fit$burnin, parameter = "alpha", level = 0.95, + decimals = 3L, ... +) { + if (is.null(burnin)) { + stop("Please specify the burnin.") + } + + stopifnot(burnin < model_fit$nmc) + stopifnot(parameter %in% c("alpha", "rho", "cluster_probs", "cluster_assignment")) + stopifnot(level > 0 && level < 1) + + + if (burnin != 0) { + df <- dplyr::filter(model_fit, .data$iteration > burnin) # removed model_fit[[parameter]] + } else { + df <- model_fit + } + + if (parameter == "alpha" || parameter == "cluster_probs") { + df <- dplyr::group_by(df, .data$cluster) + class(df) <- c("SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame") + df <- .compute_posterior_intervals(df, parameter, level, decimals) + } else if (parameter == "rho") { + decimals <- 0 + df <- dplyr::group_by(df, .data$cluster, .data$item) + class(df) <- c("SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame") + df <- .compute_posterior_intervals(df, parameter, level, decimals, discrete = TRUE) + } + + df <- dplyr::ungroup(df) + + if (model_fit$n_clusters[1] == 1) { + df <- dplyr::select(df, -.data$cluster) + } + + return(df) +} -.compute_posterior_intervals <- function(df, parameter, level, decimals, discrete = FALSE){ +.compute_posterior_intervals.BayesMallows <- function( + df, parameter, level, decimals, discrete = FALSE, ... +){ dplyr::do(df, { format <- paste0("%.", decimals, "f") @@ -112,6 +169,73 @@ compute_posterior_intervals <- function(model_fit, burnin = model_fit$burnin, } + central <- unique(stats::quantile(.data$value, probs = c((1 - level) / 2, level + (1 - level) / 2))) + central <- sprintf(format, central) + central <- paste0("[", paste(central, collapse = ","), "]") + + dplyr::tibble( + parameter = parameter, + mean = posterior_mean, + median = posterior_median, + conf_level = paste(level * 100, "%"), + hpdi = hpdi, + central_interval = central + ) + }) +} + +# same as compute_posterior_intervals, but removed the bayesmallows object and +# some other columns +.compute_posterior_intervals.SMCMallows <- function( + df, parameter, level, decimals, discrete = FALSE, ... +) { + dplyr::do(df, { + format <- paste0("%.", decimals, "f") + + posterior_mean <- round(base::mean(.data$value), decimals) + posterior_median <- round(stats::median(.data$value), decimals) + + if (discrete) { + df <- dplyr::group_by(.data, .data$value) + df <- dplyr::summarise(df, n = dplyr::n()) + df <- dplyr::arrange(df, dplyr::desc(.data$n)) + df <- dplyr::mutate(df, + cumprob = cumsum(.data$n) / sum(.data$n), + lagcumprob = dplyr::lag(.data$cumprob, default = 0) + ) + + df <- dplyr::filter(df, .data$lagcumprob <= level) + + values <- sort(dplyr::pull(df, .data$value)) + + # Find contiguous regions + breaks <- c(0, which(diff(values) != 1), length(values)) + + hpdi <- lapply( + X = seq(length(breaks) - 1), + FUN = function(.x, values, breaks) { + vals <- values[(breaks[.x] + 1):breaks[.x + 1]] + vals <- unique(c(min(vals), max(vals))) + paste0("[", paste(vals, collapse = ","), "]") + }, + values = values, + breaks = breaks + ) + hpdi <- paste(hpdi, collapse = ",") + } else { + hpdi <- HDInterval::hdi(.data$value, credMass = level, allowSplit = TRUE) + + hpdi[] <- sprintf(format, hpdi) + if (is.matrix(hpdi)) { + # Discontinous case + hpdi <- paste(apply(hpdi, 1, function(x) paste0("[", x[[1]], ",", x[[2]], "]"))) + } else { + # Continuous case + hpdi <- paste0("[", hpdi[[1]], ",", hpdi[[2]], "]") + } + } + + central <- unique(stats::quantile(.data$value, probs = c((1 - level) / 2, level + (1 - level) / 2))) central <- sprintf(format, central) central <- paste0("[", paste(central, collapse = ","), "]") diff --git a/R/data.R b/R/data.R index de3207e6..5b2f874e 100644 --- a/R/data.R +++ b/R/data.R @@ -29,3 +29,21 @@ #' #' @references \insertAllCited{} "sushi_rankings" + +#' +#' +#' A syntetic 3D matrix (\code{n_users}, \code{n_items}, \code{Time}) generated +#' using the sample_mallows function. These are test datasets used to run +#' the SMC-Mallows framework for the cases where we know all of the users +#' in our system and their original ranking information are partial rankings. +#' However at some point in time, we observe extra information about +#' an existing user in the form of a rank for an item that was previously +#' not known (\code{NA}). These datasets are very contrived as the first +#' time step (\code{sample_dataset[, , 1]}) we observed the top \code{m / 2} +#' items from each user, where \code{m} is the number of items in a ranking. +#' Then, as we increase the time, we observe the next top ranked item from +#' one user at a time, then the next top ranked item, and so on until we have +#' a complete dataset at \code{sample_dataset[, , Time]}. +#' +#' @references https://github.com/anjastein/SMC-Mallows/tree/main/data +"sample_dataset" \ No newline at end of file diff --git a/R/misc_likelihood.R b/R/misc_likelihood.R index 6100ce1c..a941e819 100644 --- a/R/misc_likelihood.R +++ b/R/misc_likelihood.R @@ -29,12 +29,13 @@ log_lik_db <- function(rho, alpha, metric, rankings, obs_freq){ } else{ card <- pfd$values[[1]] } + log_lik <- -( alpha * rank_dist_sum(rankings = t(rankings), rho = rho, metric = metric, obs_freq = obs_freq) + N * get_partition_function( alpha = alpha * n_items, n_items = n_items, metric = metric, - cardinalities = card)) + cardinalities = card)) #TODO #91: write this part in particular as new function? } return(log_lik) @@ -77,4 +78,3 @@ log_lik_db_mix <- function(rho, alpha, weights, metric, log_lik <- sum(log(weights%*%temp)) return(log_lik) } - diff --git a/R/smc_post_processing_functions.R b/R/smc_post_processing_functions.R new file mode 100644 index 00000000..492da009 --- /dev/null +++ b/R/smc_post_processing_functions.R @@ -0,0 +1,421 @@ +#' @importFrom graphics mtext par + +#' @title SMC Processing +#' @author Anja Stein +#' @param output input +#' @param colnames colnames +# AS: edited this function to include parameter `colnames`. This resolve issues in #118 with post processing functions not printing the names of items in rankings. +# The `default` is set to NULL so tat we do not cause plotting issues in `plot_rho_heatplot. +smc_processing <- function(output, colnames = NULL) { + + df <- data.frame(data = output) + + # if colnames are specified, then incorporate them + if(is.null(colnames)){ + n_items <- ncol(df) + cletters <- rep(c("Item"), times = n_items) + cindexes <- (c(1:n_items)) + cnames <- c(paste(cletters, cindexes, sep = " ")) + colnames(df) <- cnames + } else { + colnames(df) <- colnames + } + new_df <- stats::reshape( + df, + direction = "long", + varying = names(df), + new.row.names = seq_len(prod(dim(df))), + v.names = "value", + timevar = "item", + idvar = NULL, + times = names(df) + ) + attr(x = new_df, "reshapeLong") <- NULL # preserves identity to gather output + class(new_df) <- c("SMCMallows", "data.frame") + return(new_df) +} + +#' Compute Consensus Ranking +#' +#' Compute the consensus ranking using either cumulative probability (CP) or maximum a posteriori (MAP) consensus +#' \insertCite{vitelli2018}{BayesMallows}. For mixture models, the +#' consensus is given for each mixture. +#' +#' @param model_fit An object returned from \code{\link{compute_mallows}}. +#' +#' @param type Character string specifying which consensus to compute. Either +#' \code{"CP"} or \code{"MAP"}. Defaults to \code{"CP"}. +#' +#' @param burnin A numeric value specifying the number of iterations +#' to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be +#' provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}. +#' @author Anja Stein +#' +compute_consensus_smc <- function(model_fit, type, burnin) { + if (type == "CP") { + .compute_cp_consensus_smc(model_fit, burnin = burnin) + } else if (type == "MAP") { + .compute_map_consensus_smc(model_fit, burnin = burnin) + } +} + +.compute_cp_consensus_smc <- function(model_fit, burnin){ +#TODO #80: this function already exists on compute_consensus.R. Add S3 method. + + if(is.null(burnin)){ + stop("Please specify the burnin.") + } + + stopifnot(burnin < model_fit$nmc) + + # Filter out the pre-burnin iterations + + if(burnin!=0){ + df <- dplyr::filter(model_fit, .data$iteration > burnin) + }else {df <- model_fit} + + # Find the problem dimensions + n_rows <- nrow(dplyr::distinct(df, .data$item, .data$cluster)) + + # Check that there are rows. + stopifnot(n_rows > 0) + + # Check that the number of rows are consistent with the information in + # the model object + stopifnot(model_fit$n_clusters * model_fit$n_items == n_rows) + + # Convert items and clustr to character, since factor levels are not needed in this case + df <- dplyr::mutate_at(df, dplyr::vars(.data$item, .data$cluster), as.character) + + # Group by item, cluster, and value + df <- dplyr::group_by(df, .data$item, .data$cluster, .data$value) + + # Find the count of each unique combination (value, item, cluster) + df <- dplyr::count(df) + + # Arrange according to value, per item and cluster + df <- dplyr::ungroup(df) + df <- dplyr::group_by(df, .data$item, .data$cluster) + df <- dplyr::arrange(df, .data$value, .by_group = TRUE) + + # Find the cumulative probability, by dividing by the total + # count in (item, cluster) and the summing cumulatively + df <- dplyr::mutate(df, cumprob = cumsum(.data$n/sum(.data$n))) + + # Find the CP consensus per cluster, using the find_cpc_smc function + df <- dplyr::ungroup(df) + df <- dplyr::group_by(df, .data$cluster) + df <- dplyr::do(df, find_cpc_smc(.data)) + df <- dplyr::ungroup(df) + + # If there is only one cluster, we drop the cluster column + if (model_fit$n_clusters[1] == 1) { + df <- dplyr::select(df, -.data$cluster) + } + + return(df) + +} + + +# Internal function for finding CP consensus. +find_cpc_smc <- function(group_df){ +#TODO #80: this function already exists on compute_consensus.R. Add S3 method. + # Declare the result dataframe before adding rows to it + result <- dplyr::tibble( + cluster = character(), + ranking = numeric(), + item = character(), + cumprob = numeric() + ) + n_items <- max(group_df$value) + for(i in seq(from = 1, to = n_items, by = 1)){ + # Filter out the relevant rows + tmp_df <- dplyr::filter(group_df, group_df$value == i) + + # Remove items in result + tmp_df <- dplyr::anti_join(tmp_df, result, by = c("cluster", "item")) + + # Keep the max only. This filtering must be done after the first filter, + # since we take the maximum among the filtered values + if (nrow(tmp_df) >= 1) { + tmp_df <- dplyr::filter(tmp_df, .data$cumprob == max(.data$cumprob)) + } + + # Add the ranking + tmp_df <- dplyr::mutate(tmp_df, ranking = i) + + # Select the columns we want to keep, and put them in result + result <- dplyr::bind_rows( + result, + dplyr::select( + tmp_df, .data$cluster, .data$ranking, .data$item, .data$cumprob + ) + ) + + } + return(result) +} + + #AS: added one extra line of code to resolve of the issues in #118 with plotting too many rows in compute_rho_consensus +.compute_map_consensus_smc <- function(model_fit, burnin = model_fit$burnin){ +#TODO #80: this function already exists on compute_consensus.R. Add S3 method. + + if(is.null(burnin)){ + stop("Please specify the burnin.") + } + + if(burnin != 0){ + df <- dplyr::filter(model_fit, .data$iteration > burnin) + } else { + df <- model_fit + } + + # Store the total number of iterations after burnin + n_samples <- length(unique(df$iteration)) + + #----------------------------------------------------------- + #AS: remove the column n_clusters, parameter + df <- within(df, {n_clusters <- NULL; parameter <- NULL}) + #------------------------------------------------------------ + + # Spread to get items along columns + df <- stats::reshape( + data = as.data.frame(df), + direction = "wide", + idvar = c("iteration", "cluster"), + timevar = "item", + varying = list(unique(df$item)) + ) + attr(df, "reshapeWide") <- NULL # maintain identity to spread() output + + # Group by everything except iteration, and count the unique combinations + df <- dplyr::group_by_at(df, .vars = dplyr::vars(-.data$iteration)) + df <- dplyr::count(df) + df <- dplyr::ungroup(df) + # Keep only the maximum per cluster + df <- dplyr::group_by(df, .data$cluster) + df <- dplyr::mutate(df, n_max = max(.data$n)) + df <- dplyr::filter(df, .data$n == .data$n_max) + df <- dplyr::ungroup(df) + + # Compute the probability + df <- dplyr::mutate(df, probability = .data$n / n_samples) + df <- dplyr::select(df, -.data$n_max, -.data$n) + + # Now collect one set of ranks per cluster + df <- stats::reshape( + as.data.frame(df), + direction = "long", + varying = setdiff(names(df), c("cluster", "probability")), + new.row.names = seq_len(prod(dim(df))), + v.names = "map_ranking", + timevar = "item", + idvar = NULL, + times = setdiff(names(df), c("cluster", "probability")) + ) + attr(x = df, "reshapeLong") <- NULL # preserves identity to gather() output + + # Sort according to cluster and ranking + df <- dplyr::arrange(df, .data$cluster, .data$map_ranking) + + if (model_fit$n_clusters[1] == 1) { + df <- dplyr::select(df, -.data$cluster) + } + + return(df) + +} + +#' @title Compute Posterior Intervals Rho +#' @description posterior confidence intervals for rho +#' @inheritParams smc_processing +#' @param nmc Number of Monte Carlo samples +#' @param burnin A numeric value specifying the number of iterations +#' to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be +#' provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}. +#' @param verbose if \code{TRUE}, prints the final output even if the function +#' is assigned to an object. Defaults to \code{FALSE}. +#' @export +#' @author Anja Stein +#' +# AS: added an extra inout variable `colnames`. This is called in the function `smc_processing`. +compute_posterior_intervals_rho <- function(output, nmc, burnin, colnames = NULL, verbose=FALSE) { + #---------------------------------------------------------------- + # AS: added extra input parameter + smc_plot <- smc_processing(output = output, colnames = colnames) + #---------------------------------------------------------------- + smc_plot$n_clusters <- 1 + smc_plot$cluster <- "Cluster 1" + + rho_posterior_interval <- compute_posterior_intervals( + model_fit = smc_plot, burnin = burnin, + parameter = "rho", level = 0.95, decimals = 2 + ) + + #------------------------------------------------------------------------------------------ + #AS: reorder items to be in numerical order if no colnames are specified + if (is.null(colnames)) { + item_numbers <- as.numeric(gsub("\\D", "", rho_posterior_interval$item)) + mixed_order <- match(sort(item_numbers), item_numbers) + rho_posterior_interval <- rho_posterior_interval[mixed_order, ] + } + #------------------------------------------------------------------------------------------ + + if(verbose) print(rho_posterior_interval) + return(rho_posterior_interval) +} + +#' @title Compute rho consensus +#' @description MAP AND CP consensus ranking estimates +#' @inheritParams compute_posterior_intervals_rho +#' @param C C +#' @param type type +#' @export +#' @author Anja Stein +#' +# AS: added an extra inout variable `colnames`. This is called in the function `smc_processing`. +compute_rho_consensus <- function(output, nmc, burnin, C, type, colnames = NULL, verbose=FALSE) { + + n_items <- dim(output)[2] + + #---------------------------------------------------------------- + # AS: added extra input parameter + smc_plot <- smc_processing(output = output, colnames = colnames) + #---------------------------------------------------------------- + + iteration <- array(rep((1:nmc), n_items)) + smc_plot <- data.frame(data = cbind(iteration, smc_plot)) + colnames(smc_plot) <- c("iteration", "item", "value") + + smc_plot$n_clusters <- C + smc_plot$parameter <- "rho" + smc_plot$cluster <- "cluster 1" + + # rho estimation using cumulative probability + if (type == "CP") { + results <- compute_consensus_smc( + model_fit = smc_plot, type = "CP", burnin = burnin + ) + } else { + results <- compute_consensus_smc( + model_fit = smc_plot, type = "MAP", burnin = burnin + ) + } + if (verbose) print(results) + + return(results) +} + +#' @title Plot Alpha Posterior +#' @description posterior for alpha +#' @inheritParams compute_posterior_intervals_rho +#' @export +#' @author Anja Stein +#' +# AS: if you remove the verbose input variable, then the function will be consistent +# with the other plot functions(they all print when verbose=FALSE, but this function doesn't.) +#`plot_rho_heatplot` doesn't require the variable `verbose`, +# so I'm not sure if this function does to plot the density of alpha +plot_alpha_posterior <- function(output, nmc, burnin) { + alpha_samples_table <- data.frame(iteration = 1:nmc, value = output) + + plot_posterior_alpha <- ggplot2::ggplot(alpha_samples_table, ggplot2::aes_(x =~ value)) + + ggplot2::geom_density() + + ggplot2::xlab(expression(alpha)) + + ggplot2::ylab("Posterior density") + + ggplot2::ggtitle(label = "Implemented SMC scheme") + + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5)) + + print(plot_posterior_alpha) +} + +#' @title Compute Posterior Intervals Alpha +#' @description posterior confidence intervals +#' @inheritParams compute_posterior_intervals_rho +#' @export +#' @author Anja Stein +#' +compute_posterior_intervals_alpha <- function(output, nmc, burnin, verbose=FALSE) { + alpha_samples_table <- data.frame(iteration = 1:nmc, value = output) + alpha_samples_table$n_clusters <- 1 + alpha_samples_table$cluster <- "Cluster 1" + class(alpha_samples_table) <- c("SMCMallows", "data.frame") + + alpha_mixture_posterior_interval <- compute_posterior_intervals(alpha_samples_table, + burnin = burnin, + parameter = "alpha", level = 0.95, decimals = 2 + ) + if (verbose) print(alpha_mixture_posterior_interval) + return(alpha_mixture_posterior_interval) +} + + + +#' @title Plot the posterior for rho for each item +#' @param output input +#' @param nmc Number of Monte Carlo samples +#' @param burnin A numeric value specifying the number of iterations +#' to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be +#' provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}} +#' @param C Number of cluster +#' @param colnames A vector of item names. If NULL, we generate generic names for the items in the ranking. +#' @param items Either a vector of item names, or a +#' vector of indices. If NULL, five items are selected randomly. +#' @export +plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = NULL){ + + n_items = dim(output)[2] + + if(is.null(items) && n_items > 5){ + message("Items not provided by user or more than 5 items in a ranking. Picking 5 at random.") + items <- sample(1:n_items, 5, replace = F) + items = sort(items) + + } else if (is.null(items) && n_items <= 5) { + items <- c(1:n_items) + items = sort(items) + } + + # do smc processing here + smc_plot = smc_processing(output = output, colnames = colnames) + + if(!is.character(items)){ + items <- unique(smc_plot$item)[items] + } + + iteration = rep(c(1:nmc), times = n_items) + df = cbind(iteration, smc_plot) + + if(C==1){ + df = cbind(cluster = "Cluster 1", df) + } + + df <- dplyr::filter(df, .data$iteration > burnin, .data$item %in% items) + + # Compute the density, rather than the count, since the latter + # depends on the number of Monte Carlo samples + df <- dplyr::group_by(df, .data$cluster, .data$item, .data$value) + df <- dplyr::summarise(df, n = dplyr::n()) + df <- dplyr::mutate(df, pct = .data$n / sum(.data$n)) + + df$item <- factor(df$item, levels = c(items)) + + # Taken from misc.R function in BayesMallows + scalefun <- function(x) sprintf("%d", as.integer(x)) + + # Finally create the plot + p <- ggplot2::ggplot(df, ggplot2::aes(x = .data$value, y = .data$pct)) + + ggplot2::geom_col() + + ggplot2::scale_x_continuous(labels = scalefun) + + ggplot2::xlab("rank") + + ggplot2::ylab("Posterior probability") + + if(C == 1){ + p <- p + ggplot2::facet_wrap(~ .data$item) + } else { + p <- p + ggplot2::facet_wrap(~ .data$cluster + .data$item) + } + + return(p) +} diff --git a/data/data_10_6_smc.rda b/data/data_10_6_smc.rda new file mode 100644 index 0000000000000000000000000000000000000000..23c9b0b2ae6528325deed710913445abbd198f98 GIT binary patch literal 497 zcmV@B@9Ph?r+pfY?*`)m5w)p(9#=PBQupQg@%Emm z(~8L%b?73-;bJoXc%QvaC(FrVwj^o|iF%`lZ(4l{+Qj}krg~pRlJ|Aw`=6`4_oef# zerLYc!~4@*RnML4&0!t7zH8KT^!aXIMSr=uIbFQ3?tF`2>j7Wcm*dO7qt*kywl479 z_zO;*GCBCJV?+7sI{4Z#%?UDZD&O4m0(|BE|KhtVZ}64poeJNooWWO~cWQidoWWO~ z2fk9*q4>J~SP%G86~0++WbXrCTTfH?7FV{vy7NuGgRFzEy6^d8J>W}!;hS`ly|1o( zSvUCFy1-Y>)%8t3_&T~+5BSnC_-g;)tLhc`y1ctR)YuCxnkIlEZR(`;@(tNGw(0sSJ1l1o@KS%JDzBmV8yD!6+=Yy}E*M)D2 z$`|;S-_`l1-!V85e5EhW!PoBFldpbmgRk0O6TVyj&q3zkYu8VWuh!iZzR5TEin-wH n#1rAmxq6GVJ$;+Ua9Li)Rla$89@kA=Kjr!b 1}, each mixture component +\eqn{\alpha_{c}} has the same prior distribution.} + +\item{alpha_max}{Maximum value of \code{alpha} in the truncated exponential +prior distribution.} +} +\value{ +\code{alpha} or \code{alpha_prime}: Numeric value to be used + as the proposal of a new alpha +} +\description{ +Function to perform Metropolis-Hastings for new rho under + the Mallows model with footrule distance metric! +} +\details{ +\code{rankings} is a matrix of size + \eqn{N }\eqn{\times}{x}\eqn{ n_items} of rankings in each row. + Alternatively, if \eqn{N} equals 1, \code{rankings} can be a vector. +} +\examples{ +rho <- c(1,2,3,4,5,6) +alpha <- 2 +metric <- "footrule" +n_items <- 6 +rankings <- sample_mallows( + rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 +) +alpha_vector <- seq(from = 0, to = 20, by = 0.1) +iter <- 1e2 +degree <- 10 + +# Estimate the logarithm of the partition function of the Mallows rank model +# using the estimate partition function +logz_estimate <- estimate_partition_function( + method = "importance_sampling", alpha_vector = alpha_vector, + n_items = n_items, metric = "footrule", nmc = iter, degree = degree +) + +metropolis_hastings_alpha( + alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd = 0.5, + lambda = 0.1, alpha_max = 20 +) + +metropolis_hastings_alpha( + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.1, alpha_max = 20 +) + +metropolis_hastings_alpha( + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 20 +) + +metropolis_hastings_alpha( + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.15, alpha_max = 20 +) +} +\author{ +Anja Stein +} diff --git a/man/metropolis_hastings_aug_ranking.Rd b/man/metropolis_hastings_aug_ranking.Rd new file mode 100644 index 00000000..dc96c0e9 --- /dev/null +++ b/man/metropolis_hastings_aug_ranking.Rd @@ -0,0 +1,37 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{metropolis_hastings_aug_ranking} +\alias{metropolis_hastings_aug_ranking} +\title{Metropolis-Hastings Augmented Ranking} +\usage{ +metropolis_hastings_aug_ranking( + alpha, + rho, + n_items, + partial_ranking, + current_ranking, + metric +) +} +\arguments{ +\item{alpha}{Numeric value og the scale parameter} + +\item{rho}{Numeric vector specifying the consensus ranking} + +\item{n_items}{Integer is the number of items in a ranking} + +\item{partial_ranking}{An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs} + +\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function} + +\item{metric}{A character string specifying the distance metric to use in the +Bayesian Mallows Model. Available options are \code{"footrule"}, +\code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +\code{"ulam"}.} +} +\value{ +R_curr or R_obs A ranking sequence vector representing proposed augmented ranking for next iteration of MCMC chain +} +\description{ +Function to perform Metropolis-Hastings for new augmented ranking +} diff --git a/man/metropolis_hastings_aug_ranking_pseudo.Rd b/man/metropolis_hastings_aug_ranking_pseudo.Rd new file mode 100644 index 00000000..bd0e3d3e --- /dev/null +++ b/man/metropolis_hastings_aug_ranking_pseudo.Rd @@ -0,0 +1,38 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{metropolis_hastings_aug_ranking_pseudo} +\alias{metropolis_hastings_aug_ranking_pseudo} +\title{Metropolis-Hastings Augmented Ranking (pseudolikelihood)} +\usage{ +metropolis_hastings_aug_ranking_pseudo( + alpha, + rho, + n_items, + partial_ranking, + current_ranking, + metric +) +} +\arguments{ +\item{alpha}{Numeric value og the scale parameter} + +\item{rho}{Numeric vector specifying the consensus ranking} + +\item{n_items}{Integer is the number of items in a ranking} + +\item{partial_ranking}{An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs} + +\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function} + +\item{metric}{A character string specifying the distance metric to use in the +Bayesian Mallows Model. Available options are \code{"footrule"}, +\code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +\code{"ulam"}.} +} +\value{ += proposed augmented ranking or current ranking A ranking sequence vector representing proposed augmented ranking for next + iteration of MCMC chain +} +\description{ +Function to perform Metropolis-Hastings for new augmented ranking using the pseudolikelihood augmentation approach +} diff --git a/man/metropolis_hastings_rho.Rd b/man/metropolis_hastings_rho.Rd new file mode 100644 index 00000000..4f69c4d6 --- /dev/null +++ b/man/metropolis_hastings_rho.Rd @@ -0,0 +1,65 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{metropolis_hastings_rho} +\alias{metropolis_hastings_rho} +\title{Metropolis-Hastings Rho} +\usage{ +metropolis_hastings_rho(alpha, n_items, rankings, metric, rho, leap_size) +} +\arguments{ +\item{alpha}{Numeric value of the scale parameter} + +\item{n_items}{Integer is the number of items in a ranking +A matrix of size \eqn{N }\eqn{\times}{x}\eqn{ n_items} of +rankings in each row. Alternatively, if \eqn{N} equals 1, \code{rankings} +can be a vector.} + +\item{rankings}{A matrix of size \eqn{N }\eqn{\times}{x}\eqn{ n_items} of +rankings in each row. Alternatively, if \eqn{N} equals 1, \code{rankings} +can be a vector.} + +\item{metric}{Character string specifying the distance measure to use. +Available options are \code{"kendall"}, \code{"cayley"}, \code{"hamming"}, +\code{"ulam"}, \code{"footrule"} and \code{"spearman"}.} + +\item{rho}{A ranking sequence} + +\item{leap_size}{Integer specifying the step size of the leap-and-shift +proposal distribution.} +} +\description{ +Function to perform Metropolis-Hastings for new rho under the Mallows model with footrule distance metric! +} +\examples{ +rho <- t(c(1,2,3,4,5,6)) +alpha <- 2 +metric <- "footrule" +n_items <- 6 + +metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = rho, metric = metric, + rho = rho, leap_size = 1 +) + +metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = rho, metric = metric, + rho = rho, leap_size = 2 +) + +metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = rho, metric = metric, + rho = rho, leap_size = 3 +) + +rankings <- sample_mallows( + rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 +) +metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = rankings, metric = metric, + rho = rho, leap_size = 1 +) + +} +\author{ +Anja Stein +} diff --git a/man/plot_alpha_posterior.Rd b/man/plot_alpha_posterior.Rd new file mode 100644 index 00000000..482c7561 --- /dev/null +++ b/man/plot_alpha_posterior.Rd @@ -0,0 +1,23 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/smc_post_processing_functions.R +\name{plot_alpha_posterior} +\alias{plot_alpha_posterior} +\title{Plot Alpha Posterior} +\usage{ +plot_alpha_posterior(output, nmc, burnin) +} +\arguments{ +\item{output}{input} + +\item{nmc}{Number of Monte Carlo samples} + +\item{burnin}{A numeric value specifying the number of iterations +to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be +provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}.} +} +\description{ +posterior for alpha +} +\author{ +Anja Stein +} diff --git a/man/plot_rho_posterior.Rd b/man/plot_rho_posterior.Rd new file mode 100644 index 00000000..f81e962b --- /dev/null +++ b/man/plot_rho_posterior.Rd @@ -0,0 +1,27 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/smc_post_processing_functions.R +\name{plot_rho_posterior} +\alias{plot_rho_posterior} +\title{Plot the posterior for rho for each item} +\usage{ +plot_rho_posterior(output, nmc, burnin, C, colnames = NULL, items = NULL) +} +\arguments{ +\item{output}{input} + +\item{nmc}{Number of Monte Carlo samples} + +\item{burnin}{A numeric value specifying the number of iterations +to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be +provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}} + +\item{C}{Number of cluster} + +\item{colnames}{A vector of item names. If NULL, we generate generic names for the items in the ranking.} + +\item{items}{Either a vector of item names, or a +vector of indices. If NULL, five items are selected randomly.} +} +\description{ +Plot the posterior for rho for each item +} diff --git a/man/sample_dataset.Rd b/man/sample_dataset.Rd new file mode 100644 index 00000000..978705f9 --- /dev/null +++ b/man/sample_dataset.Rd @@ -0,0 +1,41 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/data.R +\docType{data} +\name{sample_dataset} +\alias{sample_dataset} +\title{A syntetic 3D matrix (\code{n_users}, \code{n_items}, \code{Time}) generated +using the sample_mallows function. These are test datasets used to run +the SMC-Mallows framework for the cases where we know all of the users +in our system and their original ranking information are partial rankings. +However at some point in time, we observe extra information about +an existing user in the form of a rank for an item that was previously +not known (\code{NA}). These datasets are very contrived as the first +time step (\code{sample_dataset[, , 1]}) we observed the top \code{m / 2} +items from each user, where \code{m} is the number of items in a ranking. +Then, as we increase the time, we observe the next top ranked item from +one user at a time, then the next top ranked item, and so on until we have +a complete dataset at \code{sample_dataset[, , Time]}.} +\format{ +An object of class \code{array} of dimension 10 x 6 x 31. +} +\usage{ +sample_dataset +} +\description{ +A syntetic 3D matrix (\code{n_users}, \code{n_items}, \code{Time}) generated +using the sample_mallows function. These are test datasets used to run +the SMC-Mallows framework for the cases where we know all of the users +in our system and their original ranking information are partial rankings. +However at some point in time, we observe extra information about +an existing user in the form of a rank for an item that was previously +not known (\code{NA}). These datasets are very contrived as the first +time step (\code{sample_dataset[, , 1]}) we observed the top \code{m / 2} +items from each user, where \code{m} is the number of items in a ranking. +Then, as we increase the time, we observe the next top ranked item from +one user at a time, then the next top ranked item, and so on until we have +a complete dataset at \code{sample_dataset[, , Time]}. +} +\references{ +https://github.com/anjastein/SMC-Mallows/tree/main/data +} +\keyword{datasets} diff --git a/man/smc_mallows_new_item_rank.Rd b/man/smc_mallows_new_item_rank.Rd new file mode 100644 index 00000000..10319151 --- /dev/null +++ b/man/smc_mallows_new_item_rank.Rd @@ -0,0 +1,64 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{smc_mallows_new_item_rank} +\alias{smc_mallows_new_item_rank} +\title{SMC-Mallows new users rank} +\usage{ +smc_mallows_new_item_rank( + n_items, + R_obs, + metric, + leap_size, + N, + Time, + logz_estimate, + mcmc_kernel_app, + alpha_prop_sd, + lambda, + alpha_max, + aug_method, + verbose = FALSE +) +} +\arguments{ +\item{n_items}{Integer is the number of items in a ranking} + +\item{R_obs}{3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps} + +\item{metric}{A character string specifying the distance metric to use in the +Bayesian Mallows Model. Available options are \code{"footrule"}, +\code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +\code{"ulam"}.} + +\item{leap_size}{leap_size Integer specifying the step size of the leap-and-shift +proposal distribution} + +\item{N}{Integer specifying the number of particles} + +\item{Time}{Integer specifying the number of time steps in the SMC algorithm} + +\item{logz_estimate}{Estimate of the partition function, computed with +\code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}.} + +\item{mcmc_kernel_app}{Interger value for the number of applications we apply the MCMC move kernel} + +\item{alpha_prop_sd}{Numeric value of the standard deviation of the prior distribution for alpha} + +\item{lambda}{Strictly positive numeric value specifying the rate parameter +of the truncated exponential prior distribution of alpha.} + +\item{alpha_max}{Maximum value of alpha in the truncated exponential +prior distribution.} + +\item{aug_method}{A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random"} + +\item{verbose}{Logical specifying whether to print out the progress of the +SMC-Mallows algorithm. Defaults to \code{FALSE}.} +} +\value{ +a 3d matrix containing the samples of rho and alpha from the SMC algorithm +} +\description{ +Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user +at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudlikelihood augmentation. +} diff --git a/man/smc_mallows_new_item_rank_alpha_fixed.Rd b/man/smc_mallows_new_item_rank_alpha_fixed.Rd new file mode 100644 index 00000000..3181e9d9 --- /dev/null +++ b/man/smc_mallows_new_item_rank_alpha_fixed.Rd @@ -0,0 +1,72 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{smc_mallows_new_item_rank_alpha_fixed} +\alias{smc_mallows_new_item_rank_alpha_fixed} +\title{SMC-Mallows new item rank (alpha fixed)} +\usage{ +smc_mallows_new_item_rank_alpha_fixed( + alpha, + n_items, + R_obs, + metric, + leap_size, + N, + Time, + logz_estimate, + mcmc_kernel_app, + alpha_prop_sd, + lambda, + alpha_max, + aug_method, + verbose = FALSE +) +} +\arguments{ +\item{alpha}{A numeric value of the true scale parameter} + +\item{n_items}{Integer is the number of items in a ranking} + +\item{R_obs}{3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps} + +\item{metric}{A character string specifying the distance metric to use in the +Bayesian Mallows Model. Available options are \code{"footrule"}, +\code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +\code{"ulam"}.} + +\item{leap_size}{leap_size Integer specifying the step size of the leap-and-shift +proposal distribution} + +\item{N}{Integer specifying the number of particles} + +\item{Time}{Integer specifying the number of time steps in the SMC algorithm} + +\item{logz_estimate}{Estimate of the partition function, computed with +\code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}.} + +\item{mcmc_kernel_app}{Integer value for the number of applications we apply the MCMC move kernel} + +\item{alpha_prop_sd}{Numeric value specifying the standard deviation of the +lognormal proposal distribution used for \eqn{\alpha} in the +Metropolis-Hastings algorithm. Defaults to \code{0.1}.} + +\item{lambda}{Strictly positive numeric value specifying the rate parameter +of the truncated exponential prior distribution of \eqn{\alpha}. Defaults +to \code{0.1}. When \code{n_cluster > 1}, each mixture component +\eqn{\alpha_{c}} has the same prior distribution.} + +\item{alpha_max}{Maximum value of \code{alpha} in the truncated exponential +prior distribution.} + +\item{aug_method}{A character string specifying the approach for filling in +the missing data, options are "pseudolikelihood" or "random".} + +\item{verbose}{Logical specifying whether to print out the progress of the +SMC-Mallows algorithm. Defaults to \code{FALSE}.} +} +\value{ +a 3d matrix containing the samples of rho and alpha from the SMC algorithm +} +\description{ +Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user +at each time step. Each correction and augmentation is done by filling in the missing item ranks randomly. +} diff --git a/man/smc_mallows_new_users_complete.Rd b/man/smc_mallows_new_users_complete.Rd new file mode 100644 index 00000000..1be80a2b --- /dev/null +++ b/man/smc_mallows_new_users_complete.Rd @@ -0,0 +1,96 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{smc_mallows_new_users_complete} +\alias{smc_mallows_new_users_complete} +\title{SMC-Mallows New Users Complete} +\usage{ +smc_mallows_new_users_complete( + R_obs, + n_items, + metric, + leap_size, + N, + Time, + mcmc_kernel_app, + num_new_obs, + alpha_prop_sd, + lambda, + alpha_max, + logz_estimate = NULL, + verbose = FALSE +) +} +\arguments{ +\item{R_obs}{Matrix containing the full set of observed rankings of size +n_assessors by n_items} + +\item{n_items}{Integer is the number of items in a ranking} + +\item{metric}{A character string specifying the distance metric to use +in the Bayesian Mallows Model. Available options are \code{"footrule"}, +\code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +\code{"ulam"}.} + +\item{leap_size}{leap_size Integer specifying the step size of the +leap-and-shift proposal distribution} + +\item{N}{Integer specifying the number of particles} + +\item{Time}{Integer specifying the number of time steps in the SMC algorithm} + +\item{mcmc_kernel_app}{Interger value for the number of applications we +apply the MCMC move kernel} + +\item{num_new_obs}{Integer value for the number of new observations +(complete rankings) for each time step} + +\item{alpha_prop_sd}{Numeric value specifying the standard deviation of the +lognormal proposal distribution used for \eqn{\alpha} in the +Metropolis-Hastings algorithm. Defaults to \code{0.1}.} + +\item{lambda}{Strictly positive numeric value specifying the rate parameter +of the truncated exponential prior distribution of \eqn{\alpha}. Defaults +to \code{0.1}. When \code{n_cluster > 1}, each mixture component +\eqn{\alpha_{c}} has the same prior distribution.} + +\item{alpha_max}{Maximum value of \code{alpha} in the truncated exponential +prior distribution.} + +\item{logz_estimate}{Estimate of the partition function, computed with +\code{\link{estimate_partition_function}} in the BayesMallow R package +{estimate_partition_function}.} + +\item{verbose}{Logical specifying whether to print out the progress of the +SMC-Mallows algorithm. Defaults to \code{FALSE}.} +} +\value{ +a set of particles each containing a value of rho and alpha +} +\description{ +Function to perform resample-move SMC algorithm where we +receive new users with complete rankings at each time step +} +\examples{ +# Generate basic elements +data <- sushi_rankings[1:100, ] +n_items <- ncol(sushi_rankings) +metric <- "footrule" +num_new_obs <- 10 +logz_estimate <- estimate_partition_function( + method = "importance_sampling", + alpha_vector = seq(from = 0, to = 15, by = 0.1), + n_items = n_items, metric = metric, nmc = 1e2, degree = 10 +) + +# Calculating rho and alpha samples +samples <- smc_mallows_new_users_complete( + R_obs = data, n_items = n_items, metric = metric, + leap_size = floor(n_items / 5), N = 100, Time = nrow(data) / num_new_obs, + mcmc_kernel_app = 5, logz_estimate = logz_estimate, + alpha_prop_sd = 0.1, lambda = 0.001, alpha_max = 1e6, + num_new_obs = num_new_obs, verbose = TRUE +) + +# Studying the structure of the output +str(samples) +} diff --git a/man/smc_mallows_new_users_partial.Rd b/man/smc_mallows_new_users_partial.Rd new file mode 100644 index 00000000..87dbf3d6 --- /dev/null +++ b/man/smc_mallows_new_users_partial.Rd @@ -0,0 +1,67 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{smc_mallows_new_users_partial} +\alias{smc_mallows_new_users_partial} +\title{SMC-Mallows new users partial} +\usage{ +smc_mallows_new_users_partial( + R_obs, + n_items, + metric, + leap_size, + N, + Time, + logz_estimate, + mcmc_kernel_app, + num_new_obs, + alpha_prop_sd, + lambda, + alpha_max, + aug_method, + verbose = FALSE +) +} +\arguments{ +\item{R_obs}{Matrix containing the full set of observed rankings of size n_assessors by n_items} + +\item{n_items}{Integer is the number of items in a ranking} + +\item{metric}{A character string specifying the distance metric to use in the +Bayesian Mallows Model. Available options are \code{"footrule"}, +\code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +\code{"ulam"}.} + +\item{leap_size}{leap_size Integer specifying the step size of the leap-and-shift +proposal distribution} + +\item{N}{Integer specifying the number of particles} + +\item{Time}{Integer specifying the number of time steps in the SMC algorithm} + +\item{logz_estimate}{Estimate of the partition function, computed with +\code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}.} + +\item{mcmc_kernel_app}{Interger value for the number of applications we apply the MCMC move kernel} + +\item{num_new_obs}{Integer value for the number of new observations (complete rankings) for each time step} + +\item{alpha_prop_sd}{Numeric value of the standard deviation of the prior distribution for alpha} + +\item{lambda}{Strictly positive numeric value specifying the rate parameter +of the truncated exponential prior distribution of alpha.} + +\item{alpha_max}{Maximum value of alpha in the truncated exponential +prior distribution.} + +\item{aug_method}{A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random"} + +\item{verbose}{Logical specifying whether to print out the progress of the +SMC-Mallows algorithm. Defaults to \code{FALSE}.} +} +\value{ +a set of particles each containing a value of rho and alpha +} +\description{ +Function to perform resample-move SMC algorithm where we receive new users with complete rankings +at each time step +} diff --git a/man/smc_mallows_new_users_partial_alpha_fixed.Rd b/man/smc_mallows_new_users_partial_alpha_fixed.Rd new file mode 100644 index 00000000..d41193aa --- /dev/null +++ b/man/smc_mallows_new_users_partial_alpha_fixed.Rd @@ -0,0 +1,55 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/RcppExports.R +\name{smc_mallows_new_users_partial_alpha_fixed} +\alias{smc_mallows_new_users_partial_alpha_fixed} +\title{SMC-mallows new users partial (alpha fixed)} +\usage{ +smc_mallows_new_users_partial_alpha_fixed( + R_obs, + n_items, + metric, + leap_size, + N, + Time, + logz_estimate, + mcmc_kernel_app, + num_new_obs, + aug_method, + alpha +) +} +\arguments{ +\item{R_obs}{Matrix containing the full set of observed rankings of size n_assessors by n_items} + +\item{n_items}{Integer is the number of items in a ranking} + +\item{metric}{A character string specifying the distance metric to use in the +Bayesian Mallows Model. Available options are \code{"footrule"}, +\code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +\code{"ulam"}.} + +\item{leap_size}{leap_size Integer specifying the step size of the leap-and-shift +proposal distribution} + +\item{N}{Integer specifying the number of particles} + +\item{Time}{Integer specifying the number of time steps in the SMC algorithm} + +\item{logz_estimate}{Estimate of the partition function, computed with +\code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}.} + +\item{mcmc_kernel_app}{Interger value for the number of applications we apply the MCMC move kernel} + +\item{num_new_obs}{Integer value for the number of new observations (complete rankings) for each time step} + +\item{aug_method}{A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random"} + +\item{alpha}{A numeric value of the scale parameter which is known and fixed} +} +\value{ +a set of particles each containing a value of rho and alpha +} +\description{ +Function to perform resample-move SMC algorithm where we receive new users with complete rankings +at each time step +} diff --git a/man/smc_processing.Rd b/man/smc_processing.Rd new file mode 100644 index 00000000..8bd9a67e --- /dev/null +++ b/man/smc_processing.Rd @@ -0,0 +1,19 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/smc_post_processing_functions.R +\name{smc_processing} +\alias{smc_processing} +\title{SMC Processing} +\usage{ +smc_processing(output, colnames = NULL) +} +\arguments{ +\item{output}{input} + +\item{colnames}{colnames} +} +\description{ +SMC Processing +} +\author{ +Anja Stein +} diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 1227a79b..6a2d0888 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -200,6 +200,295 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// calculate_backward_probability +double calculate_backward_probability(arma::uvec item_ordering, arma::vec partial_ranking, arma::vec current_ranking, arma::vec remaining_set, arma::vec rho, double alpha, int n_items, std::string metric); +RcppExport SEXP _BayesMallows_calculate_backward_probability(SEXP item_orderingSEXP, SEXP partial_rankingSEXP, SEXP current_rankingSEXP, SEXP remaining_setSEXP, SEXP rhoSEXP, SEXP alphaSEXP, SEXP n_itemsSEXP, SEXP metricSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::uvec >::type item_ordering(item_orderingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type partial_ranking(partial_rankingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type current_ranking(current_rankingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type remaining_set(remaining_setSEXP); + Rcpp::traits::input_parameter< arma::vec >::type rho(rhoSEXP); + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + rcpp_result_gen = Rcpp::wrap(calculate_backward_probability(item_ordering, partial_ranking, current_ranking, remaining_set, rho, alpha, n_items, metric)); + return rcpp_result_gen; +END_RCPP +} +// calculate_forward_probability +Rcpp::List calculate_forward_probability(arma::uvec item_ordering, arma::vec partial_ranking, arma::vec remaining_set, arma::vec rho, double alpha, int n_items, std::string metric); +RcppExport SEXP _BayesMallows_calculate_forward_probability(SEXP item_orderingSEXP, SEXP partial_rankingSEXP, SEXP remaining_setSEXP, SEXP rhoSEXP, SEXP alphaSEXP, SEXP n_itemsSEXP, SEXP metricSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::uvec >::type item_ordering(item_orderingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type partial_ranking(partial_rankingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type remaining_set(remaining_setSEXP); + Rcpp::traits::input_parameter< arma::vec >::type rho(rhoSEXP); + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + rcpp_result_gen = Rcpp::wrap(calculate_forward_probability(item_ordering, partial_ranking, remaining_set, rho, alpha, n_items, metric)); + return rcpp_result_gen; +END_RCPP +} +// correction_kernel +Rcpp::List correction_kernel(arma::vec observed_ranking, arma::vec current_ranking, int n_items); +RcppExport SEXP _BayesMallows_correction_kernel(SEXP observed_rankingSEXP, SEXP current_rankingSEXP, SEXP n_itemsSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::vec >::type observed_ranking(observed_rankingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type current_ranking(current_rankingSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + rcpp_result_gen = Rcpp::wrap(correction_kernel(observed_ranking, current_ranking, n_items)); + return rcpp_result_gen; +END_RCPP +} +// correction_kernel_pseudo +Rcpp::List correction_kernel_pseudo(arma::vec current_ranking, arma::vec observed_ranking, arma::vec rho, double alpha, int n_items, std::string metric); +RcppExport SEXP _BayesMallows_correction_kernel_pseudo(SEXP current_rankingSEXP, SEXP observed_rankingSEXP, SEXP rhoSEXP, SEXP alphaSEXP, SEXP n_itemsSEXP, SEXP metricSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::vec >::type current_ranking(current_rankingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type observed_ranking(observed_rankingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type rho(rhoSEXP); + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + rcpp_result_gen = Rcpp::wrap(correction_kernel_pseudo(current_ranking, observed_ranking, rho, alpha, n_items, metric)); + return rcpp_result_gen; +END_RCPP +} +// get_mallows_loglik +double get_mallows_loglik(double alpha, arma::vec rho, int n_items, arma::mat rankings, std::string metric); +RcppExport SEXP _BayesMallows_get_mallows_loglik(SEXP alphaSEXP, SEXP rhoSEXP, SEXP n_itemsSEXP, SEXP rankingsSEXP, SEXP metricSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< arma::vec >::type rho(rhoSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< arma::mat >::type rankings(rankingsSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + rcpp_result_gen = Rcpp::wrap(get_mallows_loglik(alpha, rho, n_items, rankings, metric)); + return rcpp_result_gen; +END_RCPP +} +// get_sample_probabilities +arma::vec get_sample_probabilities(arma::vec rho_item_rank, double alpha, arma::vec remaining_set_ranks, std::string metric, int n_items); +RcppExport SEXP _BayesMallows_get_sample_probabilities(SEXP rho_item_rankSEXP, SEXP alphaSEXP, SEXP remaining_set_ranksSEXP, SEXP metricSEXP, SEXP n_itemsSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::vec >::type rho_item_rank(rho_item_rankSEXP); + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< arma::vec >::type remaining_set_ranks(remaining_set_ranksSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + rcpp_result_gen = Rcpp::wrap(get_sample_probabilities(rho_item_rank, alpha, remaining_set_ranks, metric, n_items)); + return rcpp_result_gen; +END_RCPP +} +// leap_and_shift_probs +Rcpp::List leap_and_shift_probs(arma::vec rho, int leap_size, int n_items); +RcppExport SEXP _BayesMallows_leap_and_shift_probs(SEXP rhoSEXP, SEXP leap_sizeSEXP, SEXP n_itemsSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::vec >::type rho(rhoSEXP); + Rcpp::traits::input_parameter< int >::type leap_size(leap_sizeSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + rcpp_result_gen = Rcpp::wrap(leap_and_shift_probs(rho, leap_size, n_items)); + return rcpp_result_gen; +END_RCPP +} +// smc_mallows_new_item_rank +Rcpp::List smc_mallows_new_item_rank(unsigned int& n_items, arma::cube& R_obs, std::string& metric, int& leap_size, unsigned int& N, unsigned int Time, const Rcpp::Nullable logz_estimate, int& mcmc_kernel_app, double alpha_prop_sd, double lambda, double alpha_max, std::string& aug_method, bool verbose); +RcppExport SEXP _BayesMallows_smc_mallows_new_item_rank(SEXP n_itemsSEXP, SEXP R_obsSEXP, SEXP metricSEXP, SEXP leap_sizeSEXP, SEXP NSEXP, SEXP TimeSEXP, SEXP logz_estimateSEXP, SEXP mcmc_kernel_appSEXP, SEXP alpha_prop_sdSEXP, SEXP lambdaSEXP, SEXP alpha_maxSEXP, SEXP aug_methodSEXP, SEXP verboseSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< unsigned int& >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< arma::cube& >::type R_obs(R_obsSEXP); + Rcpp::traits::input_parameter< std::string& >::type metric(metricSEXP); + Rcpp::traits::input_parameter< int& >::type leap_size(leap_sizeSEXP); + Rcpp::traits::input_parameter< unsigned int& >::type N(NSEXP); + Rcpp::traits::input_parameter< unsigned int >::type Time(TimeSEXP); + Rcpp::traits::input_parameter< const Rcpp::Nullable >::type logz_estimate(logz_estimateSEXP); + Rcpp::traits::input_parameter< int& >::type mcmc_kernel_app(mcmc_kernel_appSEXP); + Rcpp::traits::input_parameter< double >::type alpha_prop_sd(alpha_prop_sdSEXP); + Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); + Rcpp::traits::input_parameter< double >::type alpha_max(alpha_maxSEXP); + Rcpp::traits::input_parameter< std::string& >::type aug_method(aug_methodSEXP); + Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(smc_mallows_new_item_rank(n_items, R_obs, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, alpha_prop_sd, lambda, alpha_max, aug_method, verbose)); + return rcpp_result_gen; +END_RCPP +} +// smc_mallows_new_item_rank_alpha_fixed +Rcpp::List smc_mallows_new_item_rank_alpha_fixed(double alpha, unsigned int& n_items, arma::cube& R_obs, std::string& metric, int& leap_size, unsigned int& N, unsigned int Time, const Rcpp::Nullable logz_estimate, int& mcmc_kernel_app, double alpha_prop_sd, double lambda, double alpha_max, std::string& aug_method, bool verbose); +RcppExport SEXP _BayesMallows_smc_mallows_new_item_rank_alpha_fixed(SEXP alphaSEXP, SEXP n_itemsSEXP, SEXP R_obsSEXP, SEXP metricSEXP, SEXP leap_sizeSEXP, SEXP NSEXP, SEXP TimeSEXP, SEXP logz_estimateSEXP, SEXP mcmc_kernel_appSEXP, SEXP alpha_prop_sdSEXP, SEXP lambdaSEXP, SEXP alpha_maxSEXP, SEXP aug_methodSEXP, SEXP verboseSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< unsigned int& >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< arma::cube& >::type R_obs(R_obsSEXP); + Rcpp::traits::input_parameter< std::string& >::type metric(metricSEXP); + Rcpp::traits::input_parameter< int& >::type leap_size(leap_sizeSEXP); + Rcpp::traits::input_parameter< unsigned int& >::type N(NSEXP); + Rcpp::traits::input_parameter< unsigned int >::type Time(TimeSEXP); + Rcpp::traits::input_parameter< const Rcpp::Nullable >::type logz_estimate(logz_estimateSEXP); + Rcpp::traits::input_parameter< int& >::type mcmc_kernel_app(mcmc_kernel_appSEXP); + Rcpp::traits::input_parameter< double >::type alpha_prop_sd(alpha_prop_sdSEXP); + Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); + Rcpp::traits::input_parameter< double >::type alpha_max(alpha_maxSEXP); + Rcpp::traits::input_parameter< std::string& >::type aug_method(aug_methodSEXP); + Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(smc_mallows_new_item_rank_alpha_fixed(alpha, n_items, R_obs, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, alpha_prop_sd, lambda, alpha_max, aug_method, verbose)); + return rcpp_result_gen; +END_RCPP +} +// smc_mallows_new_users_complete +Rcpp::List smc_mallows_new_users_complete(arma::mat& R_obs, int& n_items, std::string& metric, int& leap_size, int& N, int Time, int& mcmc_kernel_app, int& num_new_obs, double alpha_prop_sd, double lambda, double alpha_max, const Rcpp::Nullable& logz_estimate, bool verbose); +RcppExport SEXP _BayesMallows_smc_mallows_new_users_complete(SEXP R_obsSEXP, SEXP n_itemsSEXP, SEXP metricSEXP, SEXP leap_sizeSEXP, SEXP NSEXP, SEXP TimeSEXP, SEXP mcmc_kernel_appSEXP, SEXP num_new_obsSEXP, SEXP alpha_prop_sdSEXP, SEXP lambdaSEXP, SEXP alpha_maxSEXP, SEXP logz_estimateSEXP, SEXP verboseSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::mat& >::type R_obs(R_obsSEXP); + Rcpp::traits::input_parameter< int& >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< std::string& >::type metric(metricSEXP); + Rcpp::traits::input_parameter< int& >::type leap_size(leap_sizeSEXP); + Rcpp::traits::input_parameter< int& >::type N(NSEXP); + Rcpp::traits::input_parameter< int >::type Time(TimeSEXP); + Rcpp::traits::input_parameter< int& >::type mcmc_kernel_app(mcmc_kernel_appSEXP); + Rcpp::traits::input_parameter< int& >::type num_new_obs(num_new_obsSEXP); + Rcpp::traits::input_parameter< double >::type alpha_prop_sd(alpha_prop_sdSEXP); + Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); + Rcpp::traits::input_parameter< double >::type alpha_max(alpha_maxSEXP); + Rcpp::traits::input_parameter< const Rcpp::Nullable& >::type logz_estimate(logz_estimateSEXP); + Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(smc_mallows_new_users_complete(R_obs, n_items, metric, leap_size, N, Time, mcmc_kernel_app, num_new_obs, alpha_prop_sd, lambda, alpha_max, logz_estimate, verbose)); + return rcpp_result_gen; +END_RCPP +} +// smc_mallows_new_users_partial +Rcpp::List smc_mallows_new_users_partial(arma::mat& R_obs, unsigned int& n_items, std::string& metric, int& leap_size, unsigned int& N, unsigned int Time, const Rcpp::Nullable logz_estimate, int& mcmc_kernel_app, unsigned int& num_new_obs, double alpha_prop_sd, double lambda, double alpha_max, std::string& aug_method, bool verbose); +RcppExport SEXP _BayesMallows_smc_mallows_new_users_partial(SEXP R_obsSEXP, SEXP n_itemsSEXP, SEXP metricSEXP, SEXP leap_sizeSEXP, SEXP NSEXP, SEXP TimeSEXP, SEXP logz_estimateSEXP, SEXP mcmc_kernel_appSEXP, SEXP num_new_obsSEXP, SEXP alpha_prop_sdSEXP, SEXP lambdaSEXP, SEXP alpha_maxSEXP, SEXP aug_methodSEXP, SEXP verboseSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::mat& >::type R_obs(R_obsSEXP); + Rcpp::traits::input_parameter< unsigned int& >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< std::string& >::type metric(metricSEXP); + Rcpp::traits::input_parameter< int& >::type leap_size(leap_sizeSEXP); + Rcpp::traits::input_parameter< unsigned int& >::type N(NSEXP); + Rcpp::traits::input_parameter< unsigned int >::type Time(TimeSEXP); + Rcpp::traits::input_parameter< const Rcpp::Nullable >::type logz_estimate(logz_estimateSEXP); + Rcpp::traits::input_parameter< int& >::type mcmc_kernel_app(mcmc_kernel_appSEXP); + Rcpp::traits::input_parameter< unsigned int& >::type num_new_obs(num_new_obsSEXP); + Rcpp::traits::input_parameter< double >::type alpha_prop_sd(alpha_prop_sdSEXP); + Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); + Rcpp::traits::input_parameter< double >::type alpha_max(alpha_maxSEXP); + Rcpp::traits::input_parameter< std::string& >::type aug_method(aug_methodSEXP); + Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP); + rcpp_result_gen = Rcpp::wrap(smc_mallows_new_users_partial(R_obs, n_items, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, num_new_obs, alpha_prop_sd, lambda, alpha_max, aug_method, verbose)); + return rcpp_result_gen; +END_RCPP +} +// smc_mallows_new_users_partial_alpha_fixed +Rcpp::List smc_mallows_new_users_partial_alpha_fixed(arma::mat& R_obs, unsigned int& n_items, std::string metric, int& leap_size, unsigned int& N, unsigned int Time, const Rcpp::Nullable logz_estimate, int& mcmc_kernel_app, unsigned int& num_new_obs, std::string& aug_method, double alpha); +RcppExport SEXP _BayesMallows_smc_mallows_new_users_partial_alpha_fixed(SEXP R_obsSEXP, SEXP n_itemsSEXP, SEXP metricSEXP, SEXP leap_sizeSEXP, SEXP NSEXP, SEXP TimeSEXP, SEXP logz_estimateSEXP, SEXP mcmc_kernel_appSEXP, SEXP num_new_obsSEXP, SEXP aug_methodSEXP, SEXP alphaSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< arma::mat& >::type R_obs(R_obsSEXP); + Rcpp::traits::input_parameter< unsigned int& >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + Rcpp::traits::input_parameter< int& >::type leap_size(leap_sizeSEXP); + Rcpp::traits::input_parameter< unsigned int& >::type N(NSEXP); + Rcpp::traits::input_parameter< unsigned int >::type Time(TimeSEXP); + Rcpp::traits::input_parameter< const Rcpp::Nullable >::type logz_estimate(logz_estimateSEXP); + Rcpp::traits::input_parameter< int& >::type mcmc_kernel_app(mcmc_kernel_appSEXP); + Rcpp::traits::input_parameter< unsigned int& >::type num_new_obs(num_new_obsSEXP); + Rcpp::traits::input_parameter< std::string& >::type aug_method(aug_methodSEXP); + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + rcpp_result_gen = Rcpp::wrap(smc_mallows_new_users_partial_alpha_fixed(R_obs, n_items, metric, leap_size, N, Time, logz_estimate, mcmc_kernel_app, num_new_obs, aug_method, alpha)); + return rcpp_result_gen; +END_RCPP +} +// metropolis_hastings_alpha +double metropolis_hastings_alpha(double alpha, int n_items, arma::mat rankings, std::string metric, arma::vec rho, const Rcpp::Nullable logz_estimate, double alpha_prop_sd, double lambda, double alpha_max); +RcppExport SEXP _BayesMallows_metropolis_hastings_alpha(SEXP alphaSEXP, SEXP n_itemsSEXP, SEXP rankingsSEXP, SEXP metricSEXP, SEXP rhoSEXP, SEXP logz_estimateSEXP, SEXP alpha_prop_sdSEXP, SEXP lambdaSEXP, SEXP alpha_maxSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< arma::mat >::type rankings(rankingsSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + Rcpp::traits::input_parameter< arma::vec >::type rho(rhoSEXP); + Rcpp::traits::input_parameter< const Rcpp::Nullable >::type logz_estimate(logz_estimateSEXP); + Rcpp::traits::input_parameter< double >::type alpha_prop_sd(alpha_prop_sdSEXP); + Rcpp::traits::input_parameter< double >::type lambda(lambdaSEXP); + Rcpp::traits::input_parameter< double >::type alpha_max(alpha_maxSEXP); + rcpp_result_gen = Rcpp::wrap(metropolis_hastings_alpha(alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd, lambda, alpha_max)); + return rcpp_result_gen; +END_RCPP +} +// metropolis_hastings_aug_ranking +arma::vec metropolis_hastings_aug_ranking(double alpha, arma::vec rho, int n_items, arma::vec partial_ranking, arma::vec current_ranking, std::string metric); +RcppExport SEXP _BayesMallows_metropolis_hastings_aug_ranking(SEXP alphaSEXP, SEXP rhoSEXP, SEXP n_itemsSEXP, SEXP partial_rankingSEXP, SEXP current_rankingSEXP, SEXP metricSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< arma::vec >::type rho(rhoSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< arma::vec >::type partial_ranking(partial_rankingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type current_ranking(current_rankingSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + rcpp_result_gen = Rcpp::wrap(metropolis_hastings_aug_ranking(alpha, rho, n_items, partial_ranking, current_ranking, metric)); + return rcpp_result_gen; +END_RCPP +} +// metropolis_hastings_aug_ranking_pseudo +arma::vec metropolis_hastings_aug_ranking_pseudo(double alpha, arma::vec rho, int n_items, arma::vec partial_ranking, arma::vec current_ranking, std::string metric); +RcppExport SEXP _BayesMallows_metropolis_hastings_aug_ranking_pseudo(SEXP alphaSEXP, SEXP rhoSEXP, SEXP n_itemsSEXP, SEXP partial_rankingSEXP, SEXP current_rankingSEXP, SEXP metricSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< arma::vec >::type rho(rhoSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< arma::vec >::type partial_ranking(partial_rankingSEXP); + Rcpp::traits::input_parameter< arma::vec >::type current_ranking(current_rankingSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + rcpp_result_gen = Rcpp::wrap(metropolis_hastings_aug_ranking_pseudo(alpha, rho, n_items, partial_ranking, current_ranking, metric)); + return rcpp_result_gen; +END_RCPP +} +// metropolis_hastings_rho +arma::vec metropolis_hastings_rho(double alpha, int n_items, arma::mat rankings, std::string metric, arma::vec rho, int leap_size); +RcppExport SEXP _BayesMallows_metropolis_hastings_rho(SEXP alphaSEXP, SEXP n_itemsSEXP, SEXP rankingsSEXP, SEXP metricSEXP, SEXP rhoSEXP, SEXP leap_sizeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< double >::type alpha(alphaSEXP); + Rcpp::traits::input_parameter< int >::type n_items(n_itemsSEXP); + Rcpp::traits::input_parameter< arma::mat >::type rankings(rankingsSEXP); + Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP); + Rcpp::traits::input_parameter< arma::vec >::type rho(rhoSEXP); + Rcpp::traits::input_parameter< int >::type leap_size(leap_sizeSEXP); + rcpp_result_gen = Rcpp::wrap(metropolis_hastings_rho(alpha, n_items, rankings, metric, rho, leap_size)); + return rcpp_result_gen; +END_RCPP +} static const R_CallMethodDef CallEntries[] = { {"_BayesMallows_get_rank_distance", (DL_FUNC) &_BayesMallows_get_rank_distance, 3}, @@ -214,6 +503,22 @@ static const R_CallMethodDef CallEntries[] = { {"_BayesMallows_asymptotic_partition_function", (DL_FUNC) &_BayesMallows_asymptotic_partition_function, 6}, {"_BayesMallows_rmallows", (DL_FUNC) &_BayesMallows_rmallows, 8}, {"_BayesMallows_run_mcmc", (DL_FUNC) &_BayesMallows_run_mcmc, 27}, + {"_BayesMallows_calculate_backward_probability", (DL_FUNC) &_BayesMallows_calculate_backward_probability, 8}, + {"_BayesMallows_calculate_forward_probability", (DL_FUNC) &_BayesMallows_calculate_forward_probability, 7}, + {"_BayesMallows_correction_kernel", (DL_FUNC) &_BayesMallows_correction_kernel, 3}, + {"_BayesMallows_correction_kernel_pseudo", (DL_FUNC) &_BayesMallows_correction_kernel_pseudo, 6}, + {"_BayesMallows_get_mallows_loglik", (DL_FUNC) &_BayesMallows_get_mallows_loglik, 5}, + {"_BayesMallows_get_sample_probabilities", (DL_FUNC) &_BayesMallows_get_sample_probabilities, 5}, + {"_BayesMallows_leap_and_shift_probs", (DL_FUNC) &_BayesMallows_leap_and_shift_probs, 3}, + {"_BayesMallows_smc_mallows_new_item_rank", (DL_FUNC) &_BayesMallows_smc_mallows_new_item_rank, 13}, + {"_BayesMallows_smc_mallows_new_item_rank_alpha_fixed", (DL_FUNC) &_BayesMallows_smc_mallows_new_item_rank_alpha_fixed, 14}, + {"_BayesMallows_smc_mallows_new_users_complete", (DL_FUNC) &_BayesMallows_smc_mallows_new_users_complete, 13}, + {"_BayesMallows_smc_mallows_new_users_partial", (DL_FUNC) &_BayesMallows_smc_mallows_new_users_partial, 14}, + {"_BayesMallows_smc_mallows_new_users_partial_alpha_fixed", (DL_FUNC) &_BayesMallows_smc_mallows_new_users_partial_alpha_fixed, 11}, + {"_BayesMallows_metropolis_hastings_alpha", (DL_FUNC) &_BayesMallows_metropolis_hastings_alpha, 9}, + {"_BayesMallows_metropolis_hastings_aug_ranking", (DL_FUNC) &_BayesMallows_metropolis_hastings_aug_ranking, 6}, + {"_BayesMallows_metropolis_hastings_aug_ranking_pseudo", (DL_FUNC) &_BayesMallows_metropolis_hastings_aug_ranking_pseudo, 6}, + {"_BayesMallows_metropolis_hastings_rho", (DL_FUNC) &_BayesMallows_metropolis_hastings_rho, 6}, {NULL, NULL, 0} }; diff --git a/src/distances.cpp b/src/distances.cpp index 33aeef11..bb60ca19 100644 --- a/src/distances.cpp +++ b/src/distances.cpp @@ -93,7 +93,6 @@ double ulam_distance(const arma::vec& r1, const arma::vec& r2){ //' //' @references \insertAllCited{} //' @keywords internal -//' // [[Rcpp::export]] double get_rank_distance(arma::vec r1, arma::vec r2, std::string metric){ diff --git a/src/misc.cpp b/src/misc.cpp index c63879c9..8e70a901 100644 --- a/src/misc.cpp +++ b/src/misc.cpp @@ -79,3 +79,23 @@ arma::uvec arma_setdiff(arma::uvec x, arma::uvec y){ } return x; } + +arma::uvec maybe_offset_indices( + arma::vec& x, + arma::uvec idx_x, + const bool& quiet = true +) { + // Adjust the indices of x (i.e., idx_x) depending on whether it seems to be + // using R or C++ indices. + arma::uvec io_idx_cpp = arma::find_nonfinite(x); + arma::uvec io_idx_input = arma::sort(idx_x); + std::string message = "C++ indices detected. Unchanged."; + if (arma::any(io_idx_input - io_idx_cpp)) { + idx_x -= 1; + message = "R indices detected. Shifted."; + } + if (!quiet) { + Rcpp::Rcout << message << std::endl; + } + return(idx_x); +} diff --git a/src/misc.h b/src/misc.h index e46b7175..705b215d 100644 --- a/src/misc.h +++ b/src/misc.h @@ -9,5 +9,6 @@ arma::uvec std_setdiff(arma::uvec&, arma::uvec&); int sample_int(const arma::rowvec& probs); double rtruncbeta(int shape1, int shape2, double trunc = 1); arma::uvec arma_setdiff(arma::uvec x, arma::uvec y); +arma::uvec maybe_offset_indices(arma::vec&, arma::uvec, const bool& = true); #endif diff --git a/src/missing_data.cpp b/src/missing_data.cpp index 5c332ed6..000ab787 100644 --- a/src/missing_data.cpp +++ b/src/missing_data.cpp @@ -29,7 +29,7 @@ void initialize_missing_ranks(arma::mat& rankings, const arma::umat& missing_ind arma::conv_to::from(rank_vector(present_inds)) )); - for(int j = 0; j < missing_inds.size(); ++j){ + for(unsigned int j = 0; j < missing_inds.size(); ++j){ rank_vector(missing_inds(j)) = static_cast(arma::as_scalar(new_ranks(j))); } rankings.col(i) = rank_vector; diff --git a/src/smc.h b/src/smc.h new file mode 100644 index 00000000..c24b7865 --- /dev/null +++ b/src/smc.h @@ -0,0 +1,18 @@ +#ifndef SMC_H +#define SMC_H + +#include "RcppArmadillo.h" + +double get_mallows_loglik(double, arma::vec, int, arma::mat, std::string); +Rcpp::List leap_and_shift_probs(arma::vec, int, int); +arma::vec metropolis_hastings_rho(double, int, arma::mat, std::string, arma::vec, int); +double metropolis_hastings_alpha(double, int, arma::mat, std::string, arma::vec, const Rcpp::Nullable, double, double, double); +arma::vec get_sample_probabilities(arma::vec, double, arma::vec, std::string, int); +Rcpp::List calculate_forward_probability(arma::uvec, arma::vec, arma::vec, arma::vec, double, int, std::string); +double calculate_backward_probability(arma::uvec, arma::vec, arma::vec, arma::vec, arma::vec, double, int, std::string); +arma::vec metropolis_hastings_aug_ranking(double, arma::vec, int, arma::vec, arma::vec, std::string); +arma::vec metropolis_hastings_aug_ranking_pseudo(double, arma::vec, int, arma::vec, arma::vec, std::string); +Rcpp::List correction_kernel(arma::vec, arma::vec, int); +Rcpp::List correction_kernel_pseudo(arma::vec, arma::vec, arma::vec, double, int, std::string); + +#endif diff --git a/src/smc_calculate_backward_probability.cpp b/src/smc_calculate_backward_probability.cpp new file mode 100644 index 00000000..bbad48e9 --- /dev/null +++ b/src/smc_calculate_backward_probability.cpp @@ -0,0 +1,86 @@ +#include "RcppArmadillo.h" +#include "smc.h" +#include "misc.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Calculate Backward Probability +//' @description Function to calculate probability of assigning a set of specific ranks to an specific item +//' given its rank in the consensus ranking +//' +//' @param item_ordering A vector of integer values to represent the specified queue of which unranked item to assign a rank for the proposed augmented ranking +//' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs +//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +//' @param remaining_set A vector of integer values to represent the elements (ranks) missing from original observed ranking +//' @param rho Numeric vector specifying the consensus ranking +//' @param alpha Numeric value og the scale parameter +//' @param n_items Integer is the number of items in a ranking +//' @param metric A character string specifying the distance metric to use in the +//' Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @return backward_auxiliary_ranking_probability A numerical value of creating the previous augmented ranking using the same item ordering used to create the +//' new auggmented ranking in calculate_forward_probability funtion. +//' @export +// [[Rcpp::export]] +double calculate_backward_probability( + arma::uvec item_ordering, + arma::vec partial_ranking, + arma::vec current_ranking, + arma::vec remaining_set, + arma::vec rho, + double alpha, + int n_items, + std::string metric +) { + // given an old and new item ordering, sample ranking with new ordering and + // calc the forward and backward prob + + // show the augmented parts of the current ranking + // item ordering is the order of which items are assigned ranks in a specified + // order + arma::uword num_items_unranked = item_ordering.n_elem; + + // initialise prob of creating augmented ranking + double backward_auxiliary_ranking_probability = 1.0; + + // Adjust item_ordering depending on whether it uses R or C++ indices + item_ordering = maybe_offset_indices(partial_ranking, item_ordering); + + if (num_items_unranked != 1) { + // show the augmented parts of the current ranking + current_ranking = current_ranking.elem(item_ordering); + + /* ====================================================== */ + /* LOOP TO CALCULATE FORWARD AND BACKWARD PROBABILITY */ + /* ====================================================== */ + // given the old and new item ordering and the list of missing rank, determine + // the sample probs for each iteration + for (arma::uword jj = 0; jj < num_items_unranked - 1; ++jj) { + + // items to sample rank + arma::uword item_to_sample_rank = item_ordering(jj); + + // the rank of item in rho + arma::vec rho_item_rank; + rho_item_rank = rho(item_to_sample_rank); + + // next we get the sample probabilites for selecting a particular rank for + // an item based on the current alpha and the rho rank for that item + arma::vec sample_prob_list = get_sample_probabilities(\ + rho_item_rank, alpha, remaining_set, metric, n_items\ + ); + + // save the probability of selecting the specific item rank in the old + // augmented ranking + arma::uvec sample_prob = find(remaining_set == current_ranking(jj)); + backward_auxiliary_ranking_probability = \ + backward_auxiliary_ranking_probability * \ + arma::as_scalar(sample_prob_list(sample_prob)); + + // remove selected auxiliary rank from the set of remaining possibles + // ranks to select + remaining_set = remaining_set(find(remaining_set != current_ranking(jj))); + } + } + return backward_auxiliary_ranking_probability; +} diff --git a/src/smc_calculate_forward_probability.cpp b/src/smc_calculate_forward_probability.cpp new file mode 100644 index 00000000..64009eb1 --- /dev/null +++ b/src/smc_calculate_forward_probability.cpp @@ -0,0 +1,107 @@ +#include "RcppArmadillo.h" +#include "smc.h" +#include "misc.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Calculate Forward Probability +//' @description Function to calculate probability of assigning a set of +//' specific ranks to an specific item +//' given its rank in the consensus ranking +//' @export +//' +//' @param item_ordering A vector of integer values to represent the specified +//' queue of which unranked item to assign a rank for the proposed augmented +//' ranking +//' @param partial_ranking An incomplete rank sequence vector of the original +//' observed incomplete ranking which contains NAs +//' @param remaining_set A vector of integer values to represent the elements +//' (ranks) missing from original observed ranking +//' @param rho Numeric vector specifying the consensus ranking +//' @param alpha Numeric value og the scale parameter +//' @param n_items Integer is the number of items in a ranking +//' @param metric A character string specifying the distance metric to use in +//' the Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, +//' and \code{"ulam"}. +//' @return List containing aug_ranking, a ranking sequence vector of the +//' proposed augmented ranking and forward_prob a numerical value of the +//' probability of creating the augmented ranking using the pseudolikelihood +//' augmentation. +// [[Rcpp::export]] +Rcpp::List calculate_forward_probability( + arma::uvec item_ordering, + arma::vec partial_ranking, + arma::vec remaining_set, + arma::vec rho, + double alpha, + int n_items, + std::string metric +) { + // item ordering is the order of which items are assigned ranks in a specified + // order + arma::uword num_items_unranked = item_ordering.n_elem; + + // prob of creating augmented ranking + double forward_auxiliary_ranking_probability = 1.0; + + if (num_items_unranked == 1) { + // create new agumented ranking by sampling remaining ranks from set + // uniformly + partial_ranking.elem(find_nonfinite(partial_ranking)) = remaining_set; + } else { + arma::ivec auxiliary_ranking = Rcpp::rep(0, num_items_unranked); + + /* ====================================================== */ + /* LOOP TO CALCULATE FORWARD AND BACKWARD PROBABILITY */ + /* ====================================================== */ + // given the old and new item ordering and the list of missing rank, + // determine the sample probs for each iteration + + // Adjust item_ordering depending on whether it uses R or C++ indices + item_ordering = maybe_offset_indices(partial_ranking, item_ordering); + + for (arma::uword jj = 0; jj < num_items_unranked - 1; ++jj) { + + // items to sample rank + arma::uword item_to_sample_rank = item_ordering(jj); + + // the rank of item in rho + arma::vec rho_item_rank; + rho_item_rank = rho(item_to_sample_rank); + + // next we get the sample probabilites for selecting a particular rank for + // an item based on the current alpha and the rho rank for that item + arma::vec sample_prob_list = get_sample_probabilities(\ + rho_item_rank, alpha, remaining_set, metric, n_items\ + ); + + // fill in the new augmented ranking going forward + Rcpp::NumericVector rs, spl; + rs = remaining_set; + spl = sample_prob_list; + auxiliary_ranking(jj) = Rcpp::as(Rcpp::sample(rs, 1, false, spl)); + + // save the probability of selecting the specific item rank in the old + // augmented ranking + arma::uvec sample_prob = find(remaining_set == auxiliary_ranking(jj)); + + forward_auxiliary_ranking_probability = \ + forward_auxiliary_ranking_probability * \ + arma::as_scalar(sample_prob_list(sample_prob)); + + // remove selected auxiliary rank from the set of remaining possibles + // ranks to select + remaining_set = remaining_set(find(remaining_set != auxiliary_ranking(jj))); + } + // last element in augmented ranking is deterministic - the prob is 1 + auxiliary_ranking(num_items_unranked - 1) = arma::as_scalar(remaining_set); + + // fit the augmented ranking within the partial rankings with NAs + arma::vec ar = arma::conv_to::from(auxiliary_ranking); + partial_ranking.elem(item_ordering) = ar; // ranks for items + } + return Rcpp::List::create( + Rcpp::Named("aug_ranking") = partial_ranking, + Rcpp::Named("forward_prob") = forward_auxiliary_ranking_probability + ); +} diff --git a/src/smc_correction_kernel.cpp b/src/smc_correction_kernel.cpp new file mode 100644 index 00000000..98aee5eb --- /dev/null +++ b/src/smc_correction_kernel.cpp @@ -0,0 +1,66 @@ +#include "RcppArmadillo.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Correction Kernel +//' @description Function to determine if the augmented ranking is compatible +//' with the new observed partial ranking. If it is not, the we create a new +//' augmentation using the random sampling approachand calculate the +//' augmentation probability. +//' +//' @param current_ranking A ranking sequence vector of the current augmented +//' ranking (no missing values) +//' @param observed_ranking A ranking sequence vector of the observed partial +//' ranking (no missing values) The original incomplete partial ranking +//' is in the rankings data set. +//' @param n_items Integer is the number of items in a ranking +//' +//' @return List containing the proposed 'corrected' augmented ranking +//' that is compatible with the new observed ranking for a user +// [[Rcpp::export]] +Rcpp::List correction_kernel( + arma::vec observed_ranking, //R_obs + arma::vec current_ranking, // R_curr + int n_items +) { + // check if new information means 'mistakes' made with augmented rankings + bool observed_equals_current = arma::approx_equal(\ + observed_ranking, current_ranking, "absdiff", 0.1\ + ); + double correction_prob = 1.0; + arma::vec proposed_ranking; + if (observed_equals_current) { + proposed_ranking = current_ranking; + } else { + // resample from smaller pool of possible augmented rankings + + // find elements missing from original observed ranking + Rcpp::NumericVector o_rank_Rcpp, c_rank_Rcpp; + o_rank_Rcpp = observed_ranking; + c_rank_Rcpp = current_ranking; + arma::vec remaining_set = Rcpp::setdiff(c_rank_Rcpp, o_rank_Rcpp); + + // create new agumented ranking by sampling remaining ranks from set uniformly + proposed_ranking = observed_ranking; + + arma::uvec unranked_items = find_nonfinite(proposed_ranking); + if (remaining_set.n_elem == 1) { + proposed_ranking.elem(unranked_items) = remaining_set; + } else { + // generate random order for remaining_set + remaining_set = std::move(arma::shuffle(remaining_set)); + proposed_ranking.elem(unranked_items) = remaining_set; + } + + Rcpp::NumericVector remaining_set_Rcpp; + remaining_set_Rcpp = remaining_set; + Rcpp::NumericVector remaining_set_Rcpp_elem; + remaining_set_Rcpp_elem = remaining_set_Rcpp.length(); + Rcpp::NumericVector remaining_set_fact = Rcpp::factorial(remaining_set_Rcpp_elem); + double remaining_set_fact_dbl = Rcpp::as(remaining_set_fact); + correction_prob = 1.0 / remaining_set_fact_dbl; + } + return Rcpp::List::create( + Rcpp::Named("ranking") = proposed_ranking, + Rcpp::Named("correction_prob") = correction_prob + ); +} diff --git a/src/smc_correction_kernel_pseudo.cpp b/src/smc_correction_kernel_pseudo.cpp new file mode 100644 index 00000000..acd4f638 --- /dev/null +++ b/src/smc_correction_kernel_pseudo.cpp @@ -0,0 +1,123 @@ +#include "RcppArmadillo.h" +#include "smc.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Correction Kernel (pseudolikelihood) +//' @description Function to determine if the augmented ranking is compatible with the new observed partial ranking. +//' If it is not, the we create a new augmentation using the pseudolikelihood approach and calculate the augmentation probability. +//' +//' @param observed_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs +//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +//' @param rho Numeric vector specifying the consensus ranking +//' @param alpha Numeric value og the scale parameter +//' @param n_items Integer is the number of items in a ranking +//' @param metric A character string specifying the distance metric to use in the +//' Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @return list containing R_obs, the proposed 'corrected' augmented ranking that is compatible with the new observed ranking for a user, and +//' forward_auxiliary_ranking_probability, a numerical value for the probability of correcting the ranking to be compatible with R_obs. +// [[Rcpp::export]] +Rcpp::List correction_kernel_pseudo( + arma::vec current_ranking, //R_curr + arma::vec observed_ranking, //R_obs + arma::vec rho, + double alpha, + int n_items, + std::string metric +) { + bool observed_equals_current = arma::approx_equal(\ + observed_ranking, current_ranking, "absdiff", 0.1\ + ); + double correction_prob = 1.0; + arma::vec proposed_ranking; + if (observed_equals_current) { + return Rcpp::List::create( + Rcpp::Named("ranking") = current_ranking, + Rcpp::Named("correction_prob") = correction_prob + ); + } else { + // resample from smaller pool of possible augmented rankings + // select uniform the proposed ranking compatible with the known observed rankings + // ranks = c(1:n_items) + + // find items missing from original observed ranking + arma::uvec unranked_items = find_nonfinite(observed_ranking); + + // find elements missing from original observed ranking + Rcpp::NumericVector o_rank_Rcpp, c_rank_Rcpp; + o_rank_Rcpp = observed_ranking; + c_rank_Rcpp = current_ranking; + arma::vec remaining_set = Rcpp::setdiff(c_rank_Rcpp, o_rank_Rcpp); + + // if we only have one missing rank, then we can + if (remaining_set.n_elem == 1) { + + // create new agumented ranking by sampling remaining ranks from set uniformly + observed_ranking.elem(unranked_items) = remaining_set; + } else { + // create new agumented ranking by using pseudo proposal + Rcpp::IntegerVector unranked_items_Rcpp; + unranked_items_Rcpp = arma::conv_to::from(unranked_items); + arma::uvec item_ordering = Rcpp::as(\ + Rcpp::sample(unranked_items_Rcpp, unranked_items_Rcpp.length())\ + ); + item_ordering = item_ordering + 1; + + // item ordering is the order of which items are assigned ranks in a specified order + arma::uword num_items_unranked = item_ordering.n_elem; + + // creating now augmented ranking whilst simultaneously calculating the backwards prob of making the same + // augmented ranking with an alternative item ordering + arma::ivec auxiliary_ranking = Rcpp::rep(0, num_items_unranked); + + //######################################################## + //## Create new augmented ranking + //######################################################## + // given the item ordering and the list of missing rank, determine the sample probs for each iteration + for (arma::uword jj = 0; jj < num_items_unranked - 1; ++jj) { + + // items to sample rank + double item_to_sample_rank = item_ordering(jj); + + // the rank of item in rho + arma::vec rho_item_rank; + rho_item_rank = rho(item_to_sample_rank - 1); + + // next we get the sample probabilites for selecting a particular rank for + // an item based on the current alpha and the rho rank for that item + arma::vec sample_prob_list = get_sample_probabilities(\ + rho_item_rank, alpha, remaining_set, metric, n_items\ + ); + + // fill in the new augmented ranking going forward + Rcpp::NumericVector rs, spl; + rs = remaining_set; + spl = sample_prob_list; + auxiliary_ranking(jj) = Rcpp::as(Rcpp::sample(rs, 1, false, spl)); + + // save the probability of selecting the specific item rank in the old + // augmented ranking + arma::uvec sample_prob = find(remaining_set == auxiliary_ranking(jj)); + correction_prob = \ + correction_prob * \ + arma::as_scalar(sample_prob_list(sample_prob)); + + // remove selected auxiliary rank from the set of remaining possibles + // ranks to select + remaining_set = remaining_set(find(remaining_set != auxiliary_ranking(jj))); + } + // last element in augmented ranking is deterministic - the prob is 1 + auxiliary_ranking(num_items_unranked - 1) = arma::as_scalar(remaining_set); + + // fit the augmented ranking within the partial rankings with NAs + arma::vec ar; + ar = arma::conv_to::from(auxiliary_ranking); + observed_ranking.elem(item_ordering - 1) = ar; // ranks for items + } + return Rcpp::List::create( + Rcpp::Named("ranking") = observed_ranking, + Rcpp::Named("correction_prob") = correction_prob + ); + } +} diff --git a/src/smc_get_mallows_loglik.cpp b/src/smc_get_mallows_loglik.cpp new file mode 100644 index 00000000..b55cb9cb --- /dev/null +++ b/src/smc_get_mallows_loglik.cpp @@ -0,0 +1,72 @@ +#include "RcppArmadillo.h" +#include "distances.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Get Mallows log-likelihood +//' @description Calculates the Mallows log-likelihood given a set of rankings and a given rank sequence +//' @param alpha Numeric value of the scale parameter +//' @param rho A ranking sequence +//' @param n_items Integer is the number of items in a ranking +//' A matrix of size \eqn{N }\eqn{\times}{x}\eqn{ n_items} of +//' rankings in each row. Alternatively, if \eqn{N} equals 1, \code{rankings} +//' can be a vector. +//' @param rankings A matrix of size \eqn{N }\eqn{\times}{x}\eqn{ n_items} of +//' rankings in each row. Alternatively, if \eqn{N} equals 1, \code{rankings} +//' can be a vector. +//' @param metric Character string specifying the distance measure to use. +//' Available options are \code{"kendall"}, \code{"cayley"}, \code{"hamming"}, +//' \code{"ulam"}, \code{"footrule"} and \code{"spearman"}. +//' @return Mallows log-likelihood +//' @export +//' @author Anja Stein +//' @examples +//' set.seed(101) +//' rho <- t(c(1, 2, 3, 4, 5, 6)) +//' alpha <- 2 +//' metric <- "footrule" +//' n_items <- 6 +//' get_mallows_loglik( +//' alpha = alpha, rho = rho, n_items = length(rho), rankings = rho, +//' metric = metric +//' ) +//' +//' # return 0 because you are comparing the consensus ranking with itself +//' # if you change alpha or metric, then the result shall remain as 0 +//' +//' rankings <- sample_mallows( +//' rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 +//' ) +//' +//' # depending on your seed, you will get a different collection of rankings in R and C++ +//' +//' get_mallows_loglik( +//' alpha = alpha, rho = rho, n_items = n_items, rankings = rankings , +//' metric = metric +//' ) +// [[Rcpp::export]] +double get_mallows_loglik( + double alpha, + arma::vec rho, + int n_items, + arma::mat rankings, + std::string metric +) { + double sum_distance = 0; + + /* Transpose matrices as needed ------------------------- */ + if (rho.n_rows == rankings.n_cols) { + rankings = rankings.t(); + } + + /* calculate the sum of the distances ------------------- */ + arma::uword num_rankings = rankings.n_cols; + if (num_rankings == 1) { + sum_distance += get_rank_distance(rho, rankings, metric); + } else { + for (arma::uword jj = 0; jj < num_rankings; ++jj) { + sum_distance += get_rank_distance(rho, rankings.col(jj), metric); + } + } + double mallows_loglik = -alpha / n_items * sum_distance; + return(mallows_loglik); +} diff --git a/src/smc_get_sample_probabilities.cpp b/src/smc_get_sample_probabilities.cpp new file mode 100644 index 00000000..fd3c7d32 --- /dev/null +++ b/src/smc_get_sample_probabilities.cpp @@ -0,0 +1,46 @@ +#include "RcppArmadillo.h" +#include "distances.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Get Sample Probabilities +//' @description Calculate probability of assigning a set of specific ranks to an specific item +//' given its rank in the consensus ranking +//' +//' @param rho_item_rank An integer value rank of an item in the current consensus ranking +//' @param alpha Numeric value og the scale parameter +//' @param remaining_set_ranks A sequence of integer values of the set of possible ranks that we can assign the item +//' @param metric A character string specifying the distance metric to use in the +//' Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @param n_items Integer is the number of items in the consensus ranking +//' @return sample_prob_list A numeric sequence of sample probabilities for selecting a specific rank given the current +//' rho_item_rank +//' @export +// [[Rcpp::export]] +arma::vec get_sample_probabilities( + arma::vec rho_item_rank, + double alpha, + arma::vec remaining_set_ranks, + std::string metric, + int n_items +) { + // define a set of probs list + unsigned int num_ranks = remaining_set_ranks.n_elem; + arma::vec sample_prob_list = Rcpp::rep(0.0, num_ranks); + + // cycle through each item and calculate its specific prob + for (arma::uword ii = 0; ii < num_ranks; ++ii) { + arma::vec item_rank = {remaining_set_ranks(ii)}; + double rank_dist = get_rank_distance(rho_item_rank, item_rank, metric); + double sample_prob = -(alpha / n_items) * rank_dist; + sample_prob_list(ii) = sample_prob; + } + + // normalise probs + double maxw = max(sample_prob_list); + arma::vec w = arma::exp(sample_prob_list - maxw); + sample_prob_list = w / arma::sum(w); + + return(sample_prob_list); +} diff --git a/src/smc_leap_and_shift_probs.cpp b/src/smc_leap_and_shift_probs.cpp new file mode 100644 index 00000000..3d489cdd --- /dev/null +++ b/src/smc_leap_and_shift_probs.cpp @@ -0,0 +1,97 @@ +#include "RcppArmadillo.h" +#include + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Leap and Shift Probabilities +//' @description Determine the new Calculates transition probabilities for proposing a new rho +//' @param rho A ranking sequence +//' @param leap_size Integer specifying the step size of the leap-and-shift +//' proposal distribution. +//' @param n_items Integer is the number of items in a ranking +//' @export +//' @return A list containing: +//' \itemize{ +//' \item \code{rho_prime} A ranking sequence proposed consensus ranking +//' \item \code{forwards_prob} Numeric value to account for transition probability from rho to rho_prime +//' \item \code{backwards_prob} Numeric Value to account for the transition probability from \code{rho_prime} to \code{rho} +//' } +//' +//' @examples +//' rho <- c(1, 2, 3, 4, 5, 6) +//' n_items <- 6 +//' +//' leap_and_shift_probs(rho, 1, n_items) +//' leap_and_shift_probs(rho, 2, n_items) +//' leap_and_shift_probs(rho, 3, n_items) +//' @author Anja Stein +//' +// [[Rcpp::export]] +Rcpp::List leap_and_shift_probs(arma::vec rho, int leap_size, int n_items) { + + // draw one u uniformly from {1,...,n} to use as index for rho + int u = Rcpp::as(Rcpp::sample(n_items, 1)); + u = u - 1; // adjusting index for easier R correspondence + + // define set of integers S, the support set for sampling new ranks + int rho_minus_leap = rho(u) - leap_size; + int rho_plus_leap = rho(u) + leap_size; + int low_bd = std::max(1, rho_minus_leap); + int max_bd = std::min(n_items, rho_plus_leap); + arma::ivec S = Rcpp::seq(low_bd, max_bd); + S = S.elem(arma::find(S != rho(u))); + + // draw a random number r from S + int r = arma::as_scalar(arma::randi(1, arma::distr_param(S.min(), S.max()))); + r = r - 1; // adjusting index for R correspondence + + // Create leap step + arma::vec rho_star = rho; + rho_star(u) = r + 1; // replace u-th entry with r + + // here, two elements are the same so we need to shift element and replace the repeated r with u + int delta = rho_star(u) - rho(u); + arma::ivec rho_prime = Rcpp::rep(0, n_items); + + // shift step + for (int i = 0; i < n_items; ++i) { + if (rho(i) == rho(u)) { + rho_prime(i) = rho_star(u); + } else if ((rho(u) < rho(i)) & (rho(i) <= rho_star(u)) & (delta > 0)) { + rho_prime(i) = rho(i) - 1; + } else if ((rho(u) > rho(i)) & (rho(i) >= rho_star(u)) & (delta < 0)) { + rho_prime(i) = rho(i) + 1; + } else { + rho_prime(i) = rho(i); + } + } + + // Define support set for ranks rho_star[u] can leap to + int rho_star_minus_leap = rho_star(u) - leap_size; + int rho_star_plus_leap = rho_star(u) + leap_size; + int S_star_min = std::max(1, rho_star_minus_leap); + int S_star_max = std::min(n_items, rho_star_plus_leap); + arma::ivec S_star; + S_star = Rcpp::seq(S_star_min, S_star_max); + S_star = S_star.elem(arma::find(S_star != rho_star(u))); + + // calculate forward and backwards probabilities + arma::vec forwards_prob, backwards_prob; + if (std::abs(rho_star(u) - rho(u)) == 1) { + // p(proposed|current) + forwards_prob = 1.0 / (n_items * S.n_elem) + 1.0 / (n_items * S_star.n_elem); + // p(current|proposed) + backwards_prob = forwards_prob; + } else { + // p(proposed|current) + forwards_prob = 1.0 / (n_items * S.n_elem); + // p(current|proposed) + backwards_prob = 1.0 / (n_items * S_star.n_elem); + } + + // return(leap_shift_list) + return Rcpp::List::create( + Rcpp::Named("rho_prime") = rho_prime, + Rcpp::Named("forwards_prob") = forwards_prob, + Rcpp::Named("backwards_prob") = backwards_prob + ); +} diff --git a/src/smc_mallows_new_item_rank.cpp b/src/smc_mallows_new_item_rank.cpp new file mode 100644 index 00000000..341ccdd1 --- /dev/null +++ b/src/smc_mallows_new_item_rank.cpp @@ -0,0 +1,356 @@ +#include "RcppArmadillo.h" +#include "misc.h" +#include "smc.h" +#include "partitionfuns.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title SMC-Mallows new users rank +//' @description Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user +//' at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudlikelihood augmentation. +//' @param n_items Integer is the number of items in a ranking +//' @param R_obs 3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps +//' @param metric A character string specifying the distance metric to use in the +//' Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @param leap_size leap_size Integer specifying the step size of the leap-and-shift +//' proposal distribution +//' @param N Integer specifying the number of particles +//' @param Time Integer specifying the number of time steps in the SMC algorithm +//' @param logz_estimate Estimate of the partition function, computed with +//' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. +//' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +//' @param alpha_prop_sd Numeric value of the standard deviation of the prior distribution for alpha +//' @param lambda Strictly positive numeric value specifying the rate parameter +//' of the truncated exponential prior distribution of alpha. +//' @param alpha_max Maximum value of alpha in the truncated exponential +//' prior distribution. +//' @param aug_method A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random" +//' @param verbose Logical specifying whether to print out the progress of the +//' SMC-Mallows algorithm. Defaults to \code{FALSE}. +//' @return a 3d matrix containing the samples of rho and alpha from the SMC algorithm +//' @export +// [[Rcpp::export]] +Rcpp::List smc_mallows_new_item_rank( + unsigned int& n_items, + arma::cube& R_obs, + std::string& metric, + int& leap_size, + unsigned int& N, + unsigned int Time, + const Rcpp::Nullable logz_estimate, + int& mcmc_kernel_app, + double alpha_prop_sd, + double lambda, + double alpha_max, + std::string& aug_method, + bool verbose = false +) { + /* ====================================================== */ + /* Initialise Phase */ + /* ====================================================== */ + + // Generate N initial samples of rho using the uniform prior + arma::cube rho_samples(N, n_items, Time, arma::fill::zeros); + for (arma::uword i = 0; i < N; ++i) { + arma::uvec items_sample = arma::randperm(n_items) + 1; + for (arma::uword j = 0; j < n_items; ++j) { + rho_samples(i, j, 0) = items_sample(j); + } + } + + /* generate alpha samples using exponential prior ------- */ + arma::mat alpha_samples(N, Time); + arma::vec alpha_samples_0 = Rcpp::rexp(N, 1); + alpha_samples.col(0) = alpha_samples_0; + + /* ====================================================== */ + /* Augment Rankings */ + /* ====================================================== */ + unsigned int num_ranks = R_obs.n_rows; + + // each particle has its own set of augmented rankings + arma::cube aug_rankings(num_ranks, n_items, N, arma::fill::zeros); + arma::cube prev_aug_rankings(num_ranks, n_items, N, arma::fill::zeros); + + // augment incomplete ranks to initialise + arma::ivec ranks = Rcpp::seq(1, n_items); + + // total correction prob + arma::vec total_correction_prob = Rcpp::rep(1.0, N); + + // iterate through each observed ranking and create new "corrected" augmented rankings + for (arma::uword ii = 0; ii < N; ++ii) { + // set t-1 generation to old as we sample for t new + prev_aug_rankings.slice(ii) = aug_rankings.slice(ii); + + // make the correction + for (arma::uword jj = 0; jj < num_ranks; ++jj) { + // fill in missing ranks based on choice of augmentation method + arma::vec R_obs_slice_0_row_jj = R_obs.slice(0).row(jj).t(); + if (aug_method == "random") { + // find elements missing from original observed ranking + arma::vec partial_ranking = R_obs_slice_0_row_jj; + Rcpp::NumericVector p_rank_Rcpp, rank_Rcpp; + p_rank_Rcpp = partial_ranking; + rank_Rcpp = ranks; + Rcpp::NumericVector remaining_set = Rcpp::setdiff(rank_Rcpp, p_rank_Rcpp); + + // create new agumented ranking by sampling remaining ranks from set uniformly + arma::vec rset; + int remaining_set_length = remaining_set.length(); + if (remaining_set_length == 1) { + rset = Rcpp::as(remaining_set); + } else { + rset = Rcpp::as(Rcpp::sample(remaining_set, remaining_set.length())); + } + partial_ranking.elem(arma::find_nonfinite(partial_ranking)) = rset; + + aug_rankings.slice(ii).row(jj) = partial_ranking.t(); + Rcpp::NumericVector remaining_set_length_Rcpp, remaining_set_length_Rcpp_fact; + remaining_set_length_Rcpp = remaining_set_length; + remaining_set_length_Rcpp_fact = Rcpp::factorial(remaining_set_length_Rcpp); + double remaining_set_length_Rcpp_fact_dbl = Rcpp::as(remaining_set_length_Rcpp_fact); + total_correction_prob(ii) = total_correction_prob(ii) * (1 / remaining_set_length_Rcpp_fact_dbl); + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + // find items missing from original observed ranking + arma::uvec unranked_items = arma::find_nonfinite(R_obs_slice_0_row_jj); + + // find unallocated ranks from original observed ranking + Rcpp::NumericVector rank_Rcpp, R_obs_slice_0_row_jj_Rcpp; + R_obs_slice_0_row_jj_Rcpp = R_obs_slice_0_row_jj; + rank_Rcpp = ranks; + Rcpp::NumericVector remaining_set = Rcpp::setdiff(rank_Rcpp, R_obs_slice_0_row_jj_Rcpp); + + // randomly permute the unranked items to give the order in which they will be allocated + arma::uvec item_ordering; + item_ordering = arma::conv_to::from(arma::shuffle(unranked_items)); + Rcpp::List proposal = calculate_forward_probability(\ + item_ordering, R_obs_slice_0_row_jj, remaining_set, rho_samples.slice(0).row(ii).t(),\ + alpha_samples(ii, 0), n_items, metric\ + ); + arma::vec a_rank = proposal["aug_ranking"]; + double f_prob = proposal["forward_prob"]; + aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)) = a_rank; + total_correction_prob(ii) *= f_prob; + } else { + Rcpp::stop(\ + "Combined choice of metric and aug_method is incompatible. ", + "The value is TRUE, so the script must end here"\ + ); + } + } + } + + /* ====================================================== */ + /* Re-weight */ + /* ====================================================== */ + + // incremental weight for each particle, based on new observed rankings + arma::vec log_inc_wgt(N, arma::fill::zeros); + + for (arma::uword ii = 0; ii < N; ++ii) { + // evaluate the log estimate of the partition function for a particular + // value of alpha + + /* Initializing variables ------------------------------- */ + const Rcpp::Nullable cardinalities = R_NilValue; + + /* Calculating log_z_alpha and log_likelihood ----------- */ + double log_z_alpha = get_partition_function(\ + n_items, alpha_samples(ii, 0), cardinalities, logz_estimate, metric\ + ); + double log_likelihood = get_mallows_loglik(\ + alpha_samples(ii, 0), rho_samples.slice(0).row(ii).t(), n_items,\ + aug_rankings.slice(ii), metric\ + ); + double log_tcp = std::log(total_correction_prob(ii)); + log_inc_wgt(ii) = log_likelihood - num_ranks * log_z_alpha - log_tcp; + } + + /* normalise weights ------------------------------------ */ + double maxw = arma::max(log_inc_wgt); + arma::vec w = arma::exp(log_inc_wgt - maxw); + arma::vec norm_wgt = w / arma::sum(w); + + /* ====================================================== */ + /* Resample */ + /* ====================================================== */ + /* Resample particles using multinomial resampling ------ */ + // Using norm_wgt_rcpp so that Rcpp::sample compiles. More details on + // https://github.com/ocbe-uio/BayesMallows/issues/90#issuecomment-866614296 + Rcpp::NumericVector norm_wgt_rcpp; + norm_wgt_rcpp = norm_wgt; + arma::uvec index; + index = Rcpp::as(Rcpp::sample(N, N, true, norm_wgt_rcpp)); + index -= 1; + rho_samples.slice(0) = rho_samples.slice(0).rows(index); + arma::vec asc = alpha_samples.col(0); + alpha_samples.col(0) = asc.elem(index); + aug_rankings = aug_rankings.slices(index); + + /* ====================================================== */ + /* Move step */ + /* ====================================================== */ + for (arma::uword ii = 0; ii < N; ++ii) { + rho_samples.slice(0).row(ii) = metropolis_hastings_rho(\ + alpha_samples(ii, 0), n_items, aug_rankings.slice(ii), metric,\ + rho_samples.slice(0).row(ii).t(), leap_size\ + ).t(); + alpha_samples(ii, 0) = metropolis_hastings_alpha(\ + alpha_samples(ii, 0), n_items, aug_rankings.slice(ii), metric,\ + rho_samples.slice(0).row(ii).t(), logz_estimate,\ + alpha_prop_sd, lambda, alpha_max\ + ); + for (arma::uword jj = 0; jj < num_ranks; ++jj) { + arma::vec mh_aug_result; + if (aug_method == "random") { + mh_aug_result = metropolis_hastings_aug_ranking(\ + alpha_samples(ii, 0), rho_samples.slice(0).row(ii).t(), n_items,\ + R_obs.slice(0).row(jj).t(), aug_rankings.slice(ii).row(jj).t(), metric\ + ); + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + mh_aug_result = metropolis_hastings_aug_ranking_pseudo(\ + alpha_samples(ii, 0), rho_samples.slice(0).row(ii).t(), n_items,\ + R_obs.slice(0).row(jj).t(), aug_rankings.slice(ii).row(jj).t(), metric\ + ); + } + aug_rankings.slice(ii).row(jj) = mh_aug_result.t(); + } + } + + /* ====================================================== */ + /* Loop for t=1,...,Time */ + /* ====================================================== */ + + for (arma::uword tt = 0; tt < Time - 1; ++tt) { + if (verbose) REprintf("iteration %i out of %i \n", tt + 1, Time - 1); + + /* New Information -------------------------------------- */ + // new observed item ranks from each user, need to update augmented rankings + rho_samples.slice(tt + 1) = rho_samples.slice(tt); + alpha_samples.col(tt + 1) = alpha_samples.col(tt); + + // total correction prob + arma::vec particle_correction_prob = Rcpp::rep(1.0, N); + + // iterate through each observed ranking and create new "corrected" + // augmented rankings + + for (arma::uword ii = 0; ii < N; ++ii) { + // set t-1 generation to old as we sample for t new + prev_aug_rankings.slice(ii) = aug_rankings.slice(ii); + + // make the correction + for (arma::uword jj = 0; jj < num_ranks; ++jj) { + if (aug_method == "random") { + Rcpp::List check_correction = correction_kernel(\ + R_obs.slice(tt + 1).row(jj).t(), aug_rankings.slice(ii).row(jj).t(),\ + n_items + ); + arma::vec c_rank = check_correction["ranking"]; + double c_prob = check_correction["correction_prob"]; + aug_rankings.slice(ii).row(jj) = c_rank.t(); + particle_correction_prob(ii) *= c_prob; + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + Rcpp::List check_correction = correction_kernel_pseudo(\ + aug_rankings.slice(ii).row(jj).t(), R_obs.slice(tt + 1).row(jj).t(),\ + rho_samples.slice(tt + 1).row(ii).t(), alpha_samples(ii, tt + 1),\ + n_items, metric + ); + arma::vec c_rank = check_correction["ranking"]; + double c_prob = check_correction["correction_prob"]; + aug_rankings.slice(ii).row(jj) = c_rank.t(); + // # these probs are in real scale + particle_correction_prob(ii) *= c_prob; + } else { + Rcpp::stop("Combined choice of metric and aug_method is incompatible"); + } + } + } + + /* ====================================================== */ + /* Re-weight */ + /* ====================================================== */ + + // incremental weight for each particle, based on new observed rankings + arma::vec log_inc_wgt(N, arma::fill::zeros); + for (arma::uword ii = 0; ii < N; ++ii) { + // evaluate the log estimate of the partition function for a particular + // value of alpha + + /* Calculating log_z_alpha and log_likelihood ----------- */ + double loglik_1 = get_mallows_loglik(\ + alpha_samples(ii, tt + 1), rho_samples.slice(tt + 1).row(ii).t(), n_items,\ + aug_rankings.slice(ii), metric\ + ); + double loglik_2 = get_mallows_loglik(\ + alpha_samples(ii, tt + 1), rho_samples.slice(tt + 1).row(ii).t(), n_items,\ + prev_aug_rankings.slice(ii), metric\ + ); + double log_pcp = std::log(particle_correction_prob(ii)); + log_inc_wgt(ii) = loglik_1 - loglik_2 - log_pcp; + } + + /* normalise weights ------------------------------------ */ + double maxw = arma::max(log_inc_wgt); + arma::vec w = arma::exp(log_inc_wgt - maxw); + arma::vec norm_wgt = w / arma::sum(w); + + /* ====================================================== */ + /* Resample */ + /* ====================================================== */ + /* Resample particles using multinomial resampling ------ */ + // Using norm_wgt_rcpp so that Rcpp::sample compiles. More details on + // https://github.com/ocbe-uio/BayesMallows/issues/90#issuecomment-866614296 + Rcpp::NumericVector norm_wgt_rcpp; + norm_wgt_rcpp = norm_wgt; + arma::uvec index; + index = Rcpp::as(Rcpp::sample(N, N, true, norm_wgt_rcpp)); + index -= 1; + rho_samples.slice(tt + 1) = rho_samples.slice(tt + 1).rows(index); + arma::vec asc = alpha_samples.col(tt + 1); + alpha_samples.col(tt + 1) = asc.elem(index); + aug_rankings = aug_rankings.slices(index); + + /* ====================================================== */ + /* Move step */ + /* ====================================================== */ + for (arma::uword ii = 0; ii < N; ++ii) { + rho_samples.slice(tt + 1).row(ii) = metropolis_hastings_rho(\ + alpha_samples(ii, tt + 1), n_items, aug_rankings.slice(ii), metric,\ + rho_samples.slice(tt + 1).row(ii).t(), leap_size\ + ).t(); + alpha_samples(ii, tt + 1) = metropolis_hastings_alpha(\ + alpha_samples(ii, tt + 1), n_items, aug_rankings.slice(ii), metric,\ + rho_samples.slice(tt + 1).row(ii).t(), logz_estimate,\ + alpha_prop_sd, lambda, alpha_max\ + ); + for (arma::uword jj = 0; jj < num_ranks; ++jj) { + arma::vec mh_aug_result; + if (aug_method == "random") { + mh_aug_result = metropolis_hastings_aug_ranking(\ + alpha_samples(ii, tt + 1), rho_samples.slice(tt + 1).row(ii).t(),\ + n_items, R_obs.slice(tt + 1).row(jj).t(),\ + aug_rankings.slice(ii).row(jj).t(), metric\ + ); + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + mh_aug_result = metropolis_hastings_aug_ranking_pseudo(\ + alpha_samples(ii, tt + 1), rho_samples.slice(tt + 1).row(ii).t(),\ + n_items, R_obs.slice(tt + 1).row(jj).t(),\ + aug_rankings.slice(ii).row(jj).t(), metric\ + ); + } + aug_rankings.slice(ii).row(jj) = mh_aug_result.t(); + } + } + } + + /* ====================================================== */ + /* Post Processing */ + /* ====================================================== */ + return Rcpp::List::create( + Rcpp::Named("rho_samples") = rho_samples, + Rcpp::Named("alpha_samples") = alpha_samples + ); +} diff --git a/src/smc_mallows_new_item_rank_alpha_fixed.cpp b/src/smc_mallows_new_item_rank_alpha_fixed.cpp new file mode 100644 index 00000000..0ca0e3b9 --- /dev/null +++ b/src/smc_mallows_new_item_rank_alpha_fixed.cpp @@ -0,0 +1,341 @@ +#include "RcppArmadillo.h" +#include "smc.h" +#include "partitionfuns.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title SMC-Mallows new item rank (alpha fixed) +//' @description Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user +//' at each time step. Each correction and augmentation is done by filling in the missing item ranks randomly. +//' @param alpha A numeric value of the true scale parameter +//' @param n_items Integer is the number of items in a ranking +//' @param R_obs 3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps +//' @param metric A character string specifying the distance metric to use in the +//' Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @param leap_size leap_size Integer specifying the step size of the leap-and-shift +//' proposal distribution +//' @param N Integer specifying the number of particles +//' @param Time Integer specifying the number of time steps in the SMC algorithm +//' @param logz_estimate Estimate of the partition function, computed with +//' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. +//' @param mcmc_kernel_app Integer value for the number of applications we apply the MCMC move kernel +//' @param alpha_prop_sd Numeric value specifying the standard deviation of the +//' lognormal proposal distribution used for \eqn{\alpha} in the +//' Metropolis-Hastings algorithm. Defaults to \code{0.1}. +//' @param lambda Strictly positive numeric value specifying the rate parameter +//' of the truncated exponential prior distribution of \eqn{\alpha}. Defaults +//' to \code{0.1}. When \code{n_cluster > 1}, each mixture component +//' \eqn{\alpha_{c}} has the same prior distribution. +//' @param alpha_max Maximum value of \code{alpha} in the truncated exponential +//' prior distribution. +//' @param aug_method A character string specifying the approach for filling in +//' the missing data, options are "pseudolikelihood" or "random". +//' @param verbose Logical specifying whether to print out the progress of the +//' SMC-Mallows algorithm. Defaults to \code{FALSE}. +//' @return a 3d matrix containing the samples of rho and alpha from the SMC algorithm +//' @export +// [[Rcpp::export]] +Rcpp::List smc_mallows_new_item_rank_alpha_fixed( + double alpha, + unsigned int& n_items, + arma::cube& R_obs, + std::string& metric, + int& leap_size, + unsigned int& N, + unsigned int Time, + const Rcpp::Nullable logz_estimate, + int& mcmc_kernel_app, + double alpha_prop_sd, + double lambda, + double alpha_max, + std::string& aug_method, + bool verbose = false +) { + /* ====================================================== */ + /* Initialise Phase */ + /* ====================================================== */ + + // Generate N initial samples of rho using the uniform prior + arma::cube rho_samples(N, n_items, Time, arma::fill::zeros); + for (arma::uword i = 0; i < N; ++i) { + arma::uvec items_sample = arma::randperm(n_items) + 1; + for (arma::uword j = 0; j < n_items; ++j) { + rho_samples(i, j, 0) = items_sample(j); + } + } + + /* ====================================================== */ + /* Augment Rankings */ + /* ====================================================== */ + unsigned int num_ranks = R_obs.n_rows; + + // each particle has its own set of augmented rankings + arma::cube aug_rankings(num_ranks, n_items, N, arma::fill::zeros); + arma::cube prev_aug_rankings(num_ranks, n_items, N, arma::fill::zeros); + + // augment incomplete ranks to initialise + arma::ivec ranks = Rcpp::seq(1, n_items); + + // total correction prob + arma::vec total_correction_prob = Rcpp::rep(1.0, N); + + // iterate through each observed ranking and create new "corrected" augmented rankings + for (arma::uword ii = 0; ii < N; ++ii) { + // set t-1 generation to old as we sample for t new + prev_aug_rankings.slice(ii) = aug_rankings.slice(ii); + + for (arma::uword jj = 0; jj < num_ranks; ++jj) { + arma::vec R_obs_slice_0_row_jj = R_obs.slice(0).row(jj).t(); + if (aug_method == "random") { + // find elements missing from original observed ranking + arma::vec partial_ranking = R_obs_slice_0_row_jj; + + Rcpp::NumericVector ranks_Cpp, partial_ranking_Cpp; + ranks_Cpp = ranks; + partial_ranking_Cpp = partial_ranking; + Rcpp::NumericVector remaining_set = Rcpp::setdiff(ranks_Cpp, partial_ranking_Cpp); + + + // create new agumented ranking by sampling remaining ranks from set uniformly + arma::vec rset; + int remaining_set_length = remaining_set.length(); + if (remaining_set_length == 1) { + rset = Rcpp::as(remaining_set); + } else { + rset = Rcpp::as(Rcpp::sample(remaining_set, remaining_set.length())); + } + partial_ranking.elem(arma::find_nonfinite(partial_ranking)) = rset; + + + aug_rankings.slice(ii).row(jj) = partial_ranking.t(); + // fill in missing ranks based on choice of augmentation method + Rcpp::NumericVector remaining_set_length_Rcpp, remaining_set_length_Rcpp_fact; + remaining_set_length_Rcpp = remaining_set_length; + remaining_set_length_Rcpp_fact = Rcpp::factorial(remaining_set_length_Rcpp); + double remaining_set_length_Rcpp_fact_dbl = Rcpp::as(remaining_set_length_Rcpp_fact); + total_correction_prob(ii) = total_correction_prob(ii) * (1 / remaining_set_length_Rcpp_fact_dbl); + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + // find items missing from original observed ranking + arma::uvec unranked_items = arma::find_nonfinite(R_obs_slice_0_row_jj); + + // find unallocated ranks from original observed ranking + Rcpp::NumericVector rank_Rcpp, R_obs_slice_0_row_jj_Rcpp; + R_obs_slice_0_row_jj_Rcpp = R_obs_slice_0_row_jj; + rank_Rcpp = ranks; + Rcpp::NumericVector remaining_set = Rcpp::setdiff(rank_Rcpp, R_obs_slice_0_row_jj_Rcpp); + + // randomly permute the unranked items to give the order in which they will be allocated + arma::uvec item_ordering; + item_ordering = arma::conv_to::from(arma::shuffle(unranked_items)); + Rcpp::List proposal = calculate_forward_probability(\ + item_ordering, R_obs_slice_0_row_jj, remaining_set, rho_samples.slice(0).row(ii).t(),\ + alpha, n_items, metric\ + ); + arma::vec a_rank = proposal["aug_ranking"]; + double f_prob = proposal["forward_prob"]; + aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)) = a_rank; + total_correction_prob(ii) *= f_prob; + } else { + Rcpp::stop(\ + "Combined choice of metric and aug_method is incompatible. ", + "The value is TRUE, so the script must end here"\ + ); + } + + } + + } + + /* ====================================================== */ + /* Re-weight */ + /* ====================================================== */ + + // incremental weight for each particle, based on new observed rankings + arma::vec log_inc_wgt(N, arma::fill::zeros); + + for (arma::uword ii = 0; ii < N; ++ii) { + // evaluate the log estimate of the partition function for a particular + // value of alpha + + /* Initializing variables ------------------------------- */ + const Rcpp::Nullable cardinalities = R_NilValue; + + /* Calculating log_z_alpha and log_likelihood ----------- */ + double log_z_alpha = get_partition_function(\ + n_items, alpha, cardinalities, logz_estimate, metric\ + ); + double log_likelihood = get_mallows_loglik(\ + alpha, rho_samples.slice(0).row(ii).t(), n_items,\ + aug_rankings.slice(ii), metric\ + ); + double log_tcp = std::log(total_correction_prob(ii)); + log_inc_wgt(ii) = log_likelihood - num_ranks * log_z_alpha - log_tcp; + } + + /* normalise weights ------------------------------------ */ + double maxw = arma::max(log_inc_wgt); + arma::vec w = arma::exp(log_inc_wgt - maxw); + arma::vec norm_wgt = w / arma::sum(w); + + /* ====================================================== */ + /* Resample */ + /* ====================================================== */ + /* Resample particles using multinomial resampling ------ */ + // Using norm_wgt_rcpp so that Rcpp::sample compiles. More details on + // https://github.com/ocbe-uio/BayesMallows/issues/90#issuecomment-866614296 + Rcpp::NumericVector norm_wgt_rcpp; + norm_wgt_rcpp = norm_wgt; + arma::uvec index; + index = Rcpp::as(Rcpp::sample(N, N, true, norm_wgt_rcpp)); + index -= 1; + // index <- sample(1:N, prob = norm_wgt, size = N, replace = T) + rho_samples.slice(0) = rho_samples.slice(0).rows(index); + aug_rankings = aug_rankings.slices(index); + + /* ====================================================== */ + /* Move step */ + /* ====================================================== */ + for (arma::uword ii = 0; ii < N; ++ii) { + rho_samples.slice(0).row(ii) = metropolis_hastings_rho(\ + alpha, n_items, aug_rankings.slice(ii), metric,\ + rho_samples.slice(0).row(ii).t(), leap_size\ + ).t(); + for (arma::uword jj = 0; jj < num_ranks; ++jj) { + arma::vec mh_aug_result; + if (aug_method == "random") { + mh_aug_result = metropolis_hastings_aug_ranking(\ + alpha, rho_samples.slice(0).row(ii).t(), n_items,\ + R_obs.slice(0).row(jj).t(), aug_rankings.slice(ii).row(jj).t(), metric\ + ); + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + mh_aug_result = metropolis_hastings_aug_ranking_pseudo(\ + alpha, rho_samples.slice(0).row(ii).t(), n_items,\ + R_obs.slice(0).row(jj).t(), aug_rankings.slice(ii).row(jj).t(), metric\ + ); + } + aug_rankings.slice(ii).row(jj) = mh_aug_result.t(); + } + } + + /* ====================================================== */ + /* Loop for t=1,...,Time */ + /* ====================================================== */ + + for (arma::uword tt = 0; tt < Time - 1; ++tt) { + if (verbose) REprintf("We are not on iteration %i out of %i \n", tt + 1, Time - 1); + + /* New Information -------------------------------------- */ + // new observed item ranks from each user, need to update augmented rankings + rho_samples.slice(tt + 1) = rho_samples.slice(tt); + + // total correction prob + arma::vec total_correction_prob = Rcpp::rep(1.0, N); + + // iterate through each observed ranking and create new "corrected" + // augmented rankings + for (arma::uword ii = 0; ii < N; ++ii) { + // set t-1 generation to old as we sample for t new + prev_aug_rankings.slice(ii) = aug_rankings.slice(ii); + + // make the correction + for (arma::uword jj = 0; jj < num_ranks; ++jj) { + if (aug_method == "random") { + Rcpp::List check_correction = correction_kernel(\ + R_obs.slice(tt + 1).row(jj).t(), aug_rankings.slice(ii).row(jj).t(),\ + n_items + ); + arma::vec c_rank = check_correction["ranking"]; + double c_prob = check_correction["correction_prob"]; + aug_rankings.slice(ii).row(jj) = c_rank.t(); + total_correction_prob(ii) *= c_prob; + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + Rcpp::List check_correction = correction_kernel_pseudo(\ + aug_rankings.slice(ii).row(jj).t(), R_obs.slice(tt + 1).row(jj).t(),\ + rho_samples.slice(tt + 1).row(ii).t(), alpha, n_items, metric\ + ); + arma::vec c_rank = check_correction["ranking"]; + double c_prob = check_correction["correction_prob"]; + aug_rankings.slice(ii).row(jj) = c_rank.t(); + // # these probs are in real scale + total_correction_prob(ii) *= c_prob; + } else { + Rcpp::stop("Combined choice of metric and aug_method is incompatible"); + } + } + } + + /* ====================================================== */ + /* Re-weight */ + /* ====================================================== */ + + // incremental weight for each particle, based on new observed rankings + arma::vec log_inc_wgt(N, arma::fill::zeros); + for (arma::uword ii = 0; ii < N; ++ii) { + // evaluate the log estimate of the partition function for a particular + // value of alpha + + /* Calculating log_z_alpha and log_likelihood ----------- */ + double loglik_1 = get_mallows_loglik(\ + alpha, rho_samples.slice(tt + 1).row(ii).t(), n_items,\ + aug_rankings.slice(ii), metric\ + ); + double loglik_2 = get_mallows_loglik(\ + alpha, rho_samples.slice(tt + 1).row(ii).t(), n_items,\ + prev_aug_rankings.slice(ii), metric\ + ); + double log_pcp = std::log(total_correction_prob(ii)); + log_inc_wgt(ii) = loglik_1 - loglik_2 - log_pcp; + } + + /* normalise weights ------------------------------------ */ + double maxw = arma::max(log_inc_wgt); + arma::vec w = arma::exp(log_inc_wgt - maxw); + arma::vec norm_wgt = w / arma::sum(w); + + /* ====================================================== */ + /* Resample */ + /* ====================================================== */ + /* Resample particles using multinomial resampling ------ */ + // Using norm_wgt_rcpp so that Rcpp::sample compiles. More details on + // https://github.com/ocbe-uio/BayesMallows/issues/90#issuecomment-866614296 + Rcpp::NumericVector norm_wgt_rcpp; + norm_wgt_rcpp = norm_wgt; + arma::uvec index; + index = Rcpp::as(Rcpp::sample(N, N, true, norm_wgt_rcpp)); + index -= 1; + rho_samples.slice(tt + 1) = rho_samples.slice(tt + 1).rows(index); + + /* ====================================================== */ + /* Move step */ + /* ====================================================== */ + for (arma::uword ii = 0; ii < N; ++ii) { + rho_samples.slice(tt + 1).row(ii) = metropolis_hastings_rho(\ + alpha, n_items, aug_rankings.slice(ii), metric,\ + rho_samples.slice(tt + 1).row(ii).t(), leap_size\ + ).t(); + for (arma::uword jj = 0; jj < num_ranks; ++jj) { + arma::vec mh_aug_result; + if (aug_method == "random") { + mh_aug_result = metropolis_hastings_aug_ranking(\ + alpha, rho_samples.slice(tt + 1).row(ii).t(),\ + n_items, R_obs.slice(tt + 1).row(jj).t(),\ + aug_rankings.slice(ii).row(jj).t(), metric\ + ); + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + mh_aug_result = metropolis_hastings_aug_ranking_pseudo(\ + alpha, rho_samples.slice(tt + 1).row(ii).t(),\ + n_items, R_obs.slice(tt + 1).row(jj).t(),\ + aug_rankings.slice(ii).row(jj).t(), metric\ + ); + } + aug_rankings.slice(ii).row(jj) = mh_aug_result.t(); + } + } + } + + /* ====================================================== */ + /* Post Processing */ + /* ====================================================== */ + return Rcpp::List::create((Rcpp::Named("rho_samples") = rho_samples)); +} diff --git a/src/smc_mallows_new_users_complete.cpp b/src/smc_mallows_new_users_complete.cpp new file mode 100644 index 00000000..e52e284e --- /dev/null +++ b/src/smc_mallows_new_users_complete.cpp @@ -0,0 +1,200 @@ +#include "RcppArmadillo.h" +#include "smc.h" +#include "partitionfuns.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title SMC-Mallows New Users Complete +//' @description Function to perform resample-move SMC algorithm where we +//' receive new users with complete rankings at each time step +//' +//' @param R_obs Matrix containing the full set of observed rankings of size +//' n_assessors by n_items +//' @param n_items Integer is the number of items in a ranking +//' @param metric A character string specifying the distance metric to use +//' in the Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @param leap_size leap_size Integer specifying the step size of the +//' leap-and-shift proposal distribution +//' @param N Integer specifying the number of particles +//' @param Time Integer specifying the number of time steps in the SMC algorithm +//' @param logz_estimate Estimate of the partition function, computed with +//' \code{\link{estimate_partition_function}} in the BayesMallow R package +//' {estimate_partition_function}. +//' @param mcmc_kernel_app Interger value for the number of applications we +//' apply the MCMC move kernel +//' @param num_new_obs Integer value for the number of new observations +//' (complete rankings) for each time step +//' @param alpha_prop_sd Numeric value specifying the standard deviation of the +//' lognormal proposal distribution used for \eqn{\alpha} in the +//' Metropolis-Hastings algorithm. Defaults to \code{0.1}. +//' @param lambda Strictly positive numeric value specifying the rate parameter +//' of the truncated exponential prior distribution of \eqn{\alpha}. Defaults +//' to \code{0.1}. When \code{n_cluster > 1}, each mixture component +//' \eqn{\alpha_{c}} has the same prior distribution. +//' @param alpha_max Maximum value of \code{alpha} in the truncated exponential +//' prior distribution. +//' @param verbose Logical specifying whether to print out the progress of the +//' SMC-Mallows algorithm. Defaults to \code{FALSE}. +//' +//' @return a set of particles each containing a value of rho and alpha +//' +//' @importFrom stats rexp +//' @export +//' +//' @example inst/examples/smc_mallows_new_users_complete_example.R +//' +// [[Rcpp::export]] +Rcpp::List smc_mallows_new_users_complete( + arma::mat& R_obs, + int& n_items, + std::string& metric, + int& leap_size, + int& N, + int Time, + int& mcmc_kernel_app, + int& num_new_obs, + double alpha_prop_sd, + double lambda, + double alpha_max, + const Rcpp::Nullable& logz_estimate = R_NilValue, + bool verbose = false +) { + + /* ====================================================== */ + /* Initialise Phase */ + /* ====================================================== */ + int n_users = R_obs.n_rows; // total number of users + if (Time > n_users / num_new_obs) { + Rcpp::warning(\ + "Time should not exceed n_users / num_new_obs. Recalculating."\ + ); + Time = n_users / num_new_obs; + } + + /* generate rho samples using uniform prior ------------- */ + arma::cube rho_samples(N, n_items, (n_users + Time + 1), arma::fill::zeros); + for (int i = 0; i < N; ++i) { + arma::uvec items_sample = arma::randperm(n_items) + 1; + for (int j = 0; j < n_items; ++j) { + rho_samples(i, j, 0) = items_sample(j); + } + } + + /* generate alpha samples using exponential prior ------- */ + arma::mat alpha_samples(N, (n_users + Time + 1)); + arma::vec alpha_samples_0 = Rcpp::rexp(N, 1); + alpha_samples.col(0) = alpha_samples_0; + + /* ====================================================== */ + /* New user situation */ + /* ====================================================== */ + int num_obs = 0; + + for (arma::uword tt = 0; tt < Time; ++tt) { + if (verbose) REprintf("observe %i out of %i \n", tt + 1, Time); + + // keep tally of how many ranking observations we have so far + num_obs = num_obs + num_new_obs; + + /* ====================================================== */ + /* New Information */ + /* ====================================================== */ + // create two ranking dataset to use for the reweight and move stages of the + // algorithm + int row_start = num_obs - num_new_obs; + arma::mat new_observed_rankings(num_obs, R_obs.n_cols); + arma::mat all_observed_rankings; + new_observed_rankings = R_obs.submat(row_start, 0, num_obs - 1, R_obs.n_cols - 1); + all_observed_rankings = R_obs.submat(0, 0, num_obs - 1, R_obs.n_cols - 1); + + // propagate particles onto the next time step + rho_samples.slice(tt + 1) = rho_samples.slice(tt); + alpha_samples.col(tt + 1) = alpha_samples.col(tt); + + /* ====================================================== */ + /* Re-weight */ + /* ====================================================== */ + + // calculate incremental weight for each particle, based on + // new observed rankings + arma::vec log_inc_wgt(N, arma::fill::zeros); + + for (int ii = 0; ii < N; ++ii) { + // evaluate the log estimate of the partition function for a particular + // value of alpha + + /* Initializing variables ------------------------------- */ + const Rcpp::Nullable cardinalities = R_NilValue; + double alpha_samples_ii = alpha_samples(ii, tt + 1); + arma::rowvec rho_samples_ii = \ + rho_samples(arma::span(ii), arma::span::all, arma::span(tt + 1)); + + /* Calculating log_z_alpha and log_likelihood ----------- */ + double log_z_alpha, log_likelihood; + log_z_alpha = get_partition_function(\ + n_items, alpha_samples_ii, cardinalities, logz_estimate, metric\ + ); + log_likelihood = get_mallows_loglik(\ + alpha_samples_ii, rho_samples_ii.t(), n_items, new_observed_rankings,\ + metric\ + ); + log_inc_wgt(ii) = log_likelihood - num_new_obs * log_z_alpha; + } + + /* normalise weights ------------------------------------ */ + double maxw = arma::max(log_inc_wgt); + arma::vec w = arma::exp(log_inc_wgt - maxw); + arma::vec norm_wgt = w / arma::sum(w); + + /* ====================================================== */ + /* Resample */ + /* ====================================================== */ + + /* Resample particles using multinomial resampling ------ */ + // Using norm_wgt_rcpp so that Rcpp::sample compiles. More details on + // https://github.com/ocbe-uio/BayesMallows/issues/90#issuecomment-866614296 + Rcpp::NumericVector norm_wgt_rcpp; + norm_wgt_rcpp = norm_wgt; + arma::uvec index, tt_vec; + index = Rcpp::as(Rcpp::sample(N, N, true, norm_wgt_rcpp)); + index = index - 1; + tt_vec = tt; + + /* Replacing tt + 1 slice on rho_samples ---------------- */ + arma::mat rho_samples_slice_11p1 = rho_samples.slice(tt + 1); + rho_samples_slice_11p1 = rho_samples_slice_11p1.rows(index); + rho_samples.slice(tt + 1) = rho_samples_slice_11p1; + + /* Replacing tt + 1 column on alpha_samples ------------- */ + alpha_samples.col(tt + 1) = alpha_samples.submat(index, tt_vec + 1); + + /* ====================================================== */ + /* Move step */ + /* ====================================================== */ + for (int ii = 0; ii < N; ++ii) { + for (int kk = 0; kk < mcmc_kernel_app; ++kk) { + // move each particle containing sample of rho and alpha by using + // the MCMC kernels + double as = alpha_samples(ii, tt + 1); + arma::rowvec rs = \ + rho_samples(arma::span(ii), arma::span::all, arma::span(tt + 1)); + rho_samples(arma::span(ii), arma::span::all, arma::span(tt + 1)) =\ + metropolis_hastings_rho(\ + as, n_items, all_observed_rankings, metric, rs.t(), leap_size\ + ); + alpha_samples(ii, tt + 1) = metropolis_hastings_alpha(\ + as, n_items, all_observed_rankings, metric, rs.t(), logz_estimate,\ + alpha_prop_sd, lambda, alpha_max\ + ); + } + } + } + // return the history of the particles and their values + Rcpp::List particle_history = Rcpp::List::create( + Rcpp::Named("rho_samples") = rho_samples, + Rcpp::Named("alpha_samples") = alpha_samples + ); + particle_history.attr("class") = "SMCMallows"; // TODO: add List + return particle_history; +} diff --git a/src/smc_mallows_new_users_partial.cpp b/src/smc_mallows_new_users_partial.cpp new file mode 100644 index 00000000..70a36d94 --- /dev/null +++ b/src/smc_mallows_new_users_partial.cpp @@ -0,0 +1,244 @@ +#include "RcppArmadillo.h" +#include "misc.h" +#include "smc.h" +#include "partitionfuns.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title SMC-Mallows new users partial +//' @description Function to perform resample-move SMC algorithm where we receive new users with complete rankings +//' at each time step +//' @param R_obs Matrix containing the full set of observed rankings of size n_assessors by n_items +//' @param n_items Integer is the number of items in a ranking +//' @param metric A character string specifying the distance metric to use in the +//' Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @param leap_size leap_size Integer specifying the step size of the leap-and-shift +//' proposal distribution +//' @param N Integer specifying the number of particles +//' @param Time Integer specifying the number of time steps in the SMC algorithm +//' @param logz_estimate Estimate of the partition function, computed with +//' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. +//' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +//' @param num_new_obs Integer value for the number of new observations (complete rankings) for each time step +//' @param alpha_prop_sd Numeric value of the standard deviation of the prior distribution for alpha +//' @param lambda Strictly positive numeric value specifying the rate parameter +//' of the truncated exponential prior distribution of alpha. +//' @param alpha_max Maximum value of alpha in the truncated exponential +//' prior distribution. +//' @param aug_method A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random" +//' @param verbose Logical specifying whether to print out the progress of the +//' SMC-Mallows algorithm. Defaults to \code{FALSE}. +//' @return a set of particles each containing a value of rho and alpha +//' @export +// [[Rcpp::export]] +Rcpp::List smc_mallows_new_users_partial( + arma::mat& R_obs, + unsigned int& n_items, + std::string& metric, + int& leap_size, + unsigned int& N, + unsigned int Time, + const Rcpp::Nullable logz_estimate, + int& mcmc_kernel_app, + unsigned int& num_new_obs, + double alpha_prop_sd, + double lambda, + double alpha_max, + std::string& aug_method, + bool verbose = false +) { + /* ====================================================== */ + /* Initialise Phase */ + /* ====================================================== */ + int n_users = R_obs.n_rows; // this is total- number of users + + /* generate rho samples using uniform prior ------------- */ + arma::cube rho_samples(N, n_items, Time + 1, arma::fill::zeros); + for (arma::uword i = 0; i < N; ++i) { + arma::uvec items_sample = arma::randperm(n_items) + 1; + for (arma::uword j = 0; j < n_items; ++j) { + rho_samples(i, j, 0) = items_sample(j); + } + } + + /* generate alpha samples using exponential prior ------- */ + arma::mat alpha_samples(N, Time + 1); + arma::vec alpha_samples_0 = Rcpp::rexp(N, 1); + alpha_samples.col(0) = alpha_samples_0; + + // this is to store the augmentations of the observed rankings for each particle + arma::cube aug_rankings(n_users, n_items, N, arma::fill::zeros); // no. users by items by particles + + /* ====================================================== */ + /* New user situation */ + /* ====================================================== */ + unsigned int num_obs = 0; + + for (arma::uword tt = 0; tt < Time; ++tt) { + if (verbose) REprintf("observe %i out of %i \n", tt + 1, Time); + /* ====================================================== */ + /* New Information */ + /* ====================================================== */ + // keep tally of how many ranking observations we have so far + num_obs = num_obs + num_new_obs; + + // propagate particles onto the next time step + rho_samples.slice(tt + 1) = rho_samples.slice(tt); + alpha_samples.col(tt + 1) = alpha_samples.col(tt); + + // calculate incremental weight and augmentation prob for each particle, + // based on new observed rankings + arma::vec log_inc_wgt(N, arma::fill::zeros); + + /* ====================================================== */ + /* Augment partial rankings */ + /* ====================================================== */ + + arma::ivec ranks = Rcpp::seq(1, n_items); + arma::vec aug_prob = Rcpp::rep(1.0, N); + + for (arma::uword ii = 0; ii < N; ++ii) { + for (arma::uword jj = num_obs - num_new_obs; jj < num_obs; ++jj) { + arma::vec partial_ranking = R_obs.row(jj).t(); + + // find items missing from original observed ranking + arma::uvec unranked_items = find_nonfinite(partial_ranking); + + // find ranks missing from ranking + Rcpp::NumericVector ranks_Cpp, partial_ranking_Cpp; + ranks_Cpp = ranks; + partial_ranking_Cpp = partial_ranking; + Rcpp::NumericVector missing_ranks = Rcpp::sort_unique(Rcpp::setdiff(ranks_Cpp, partial_ranking_Cpp)); + + // fill in missing ranks based on choice of augmentation method + if (aug_method == "random") { + + // create new agumented ranking by sampling remaining ranks from set uniformly + if (missing_ranks.length() == 1) { + partial_ranking.elem(arma::find_nonfinite(partial_ranking)) = Rcpp::as(missing_ranks); + } else { + partial_ranking.elem(arma::find_nonfinite(partial_ranking)) = Rcpp::as(Rcpp::sample(missing_ranks, missing_ranks.length())); + } + + aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)) = partial_ranking; + int missing_ranks_length = missing_ranks.length(); + int missing_ranks_length_fact = factorial(missing_ranks_length); // from misc.h + aug_prob(ii) = aug_prob(ii) * (1.0 / missing_ranks_length_fact); + + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + + // randomly permute the unranked items to give the order in which they will be allocated + arma::uvec item_ordering; + item_ordering = arma::conv_to::from(arma::shuffle(unranked_items)); + arma::rowvec rho_s = rho_samples(arma::span(ii), arma::span::all, arma::span(tt + 1)); + Rcpp::List proposal = calculate_forward_probability(\ + item_ordering, partial_ranking, missing_ranks, rho_s.t(),\ + alpha_samples(ii, tt + 1), n_items, metric\ + ); + arma::vec a_rank = proposal["aug_ranking"]; + double f_prob = proposal["forward_prob"]; + aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)) = a_rank; + aug_prob(ii) = aug_prob(ii) * f_prob; + } else { + Rcpp::stop("Combined choice of metric and aug_method is incompatible"); + } + } + } + + /* ====================================================== */ + /* Re-weight */ + /* ====================================================== */ + + for (arma::uword ii = 0; ii < N; ++ii) { + // evaluate the log estimate of the partition function for a particular + // value of alpha + + /* Initializing variables ------------------------------- */ + const Rcpp::Nullable cardinalities = R_NilValue; + double alpha_samples_ii = alpha_samples(ii, tt + 1); + arma::rowvec rho_samples_ii = \ + rho_samples(arma::span(ii), arma::span::all, arma::span(tt + 1)); + + /* Calculating log_z_alpha and log_likelihood ----------- */ + double log_z_alpha, log_likelihood; + log_z_alpha = get_partition_function(\ + n_items, alpha_samples_ii, cardinalities, logz_estimate, metric\ + ); + + arma::mat new_observed_rankings; + new_observed_rankings = aug_rankings(arma::span(num_obs - num_new_obs, num_obs - 1), arma::span::all, arma::span(ii)); + log_likelihood = get_mallows_loglik(\ + alpha_samples_ii, rho_samples_ii.t(), n_items, new_observed_rankings,\ + metric\ + ); + log_inc_wgt(ii) = log_likelihood - num_new_obs * log_z_alpha; + } + + /* normalise weights ------------------------------------ */ + double maxw = arma::max(log_inc_wgt); + arma::vec w = arma::exp(log_inc_wgt - maxw); + arma::vec norm_wgt = w / arma::sum(w); + + /* ====================================================== */ + /* Resample */ + /* ====================================================== */ + + /* Resample particles using multinomial resampling ------ */ + // Using norm_wgt_rcpp so that Rcpp::sample compiles. More details on + // https://github.com/ocbe-uio/BayesMallows/issues/90#issuecomment-866614296 + Rcpp::NumericVector norm_wgt_rcpp; + norm_wgt_rcpp = norm_wgt; + arma::uvec tt_vec, indices; + indices = Rcpp::as(Rcpp::sample(N, N, true, norm_wgt_rcpp)); + indices = indices - 1; + tt_vec = tt; + + /* Replacing tt + 1 slice on rho_samples ---------------- */ + arma::mat rho_samples_slice_11p1 = rho_samples.slice(tt + 1); + rho_samples_slice_11p1 = rho_samples_slice_11p1.rows(indices); + rho_samples.slice(tt + 1) = rho_samples_slice_11p1; + + /* Replacing tt + 1 column on alpha_samples ------------- */ + alpha_samples.col(tt + 1) = alpha_samples.submat(indices, tt_vec + 1); + arma::cube aug_rankings_indices = aug_rankings.slices(indices); + aug_rankings.rows(0, num_obs - 1) = aug_rankings_indices(arma::span(0, num_obs - 1), arma::span::all, arma::span::all); + + /* ====================================================== */ + /* Move step */ + /* ====================================================== */ + for (arma::uword ii = 0; ii < N; ++ii) { + double as = alpha_samples(ii, tt + 1); + arma::mat all_observed_rankings; + all_observed_rankings = aug_rankings(arma::span(0, num_obs - 1), arma::span::all, arma::span(ii)); + arma::mat rs_slice = rho_samples.slice(tt + 1); + arma::rowvec rs = rs_slice.row(ii); + // move each particle containing sample of rho and alpha by using + // the MCMC kernels + rho_samples(arma::span(ii), arma::span::all, arma::span(tt + 1)) =\ + metropolis_hastings_rho(\ + as, n_items, all_observed_rankings, metric, rs.t(), leap_size\ + ); + alpha_samples(ii, tt + 1) = metropolis_hastings_alpha(\ + as, n_items, all_observed_rankings, metric, rs.t(), logz_estimate,\ + alpha_prop_sd, lambda, alpha_max\ + ); + for (arma::uword jj = 0; jj < num_obs; ++jj) { + arma::rowvec ar; + ar = aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)); + arma::vec mh_aug_result; + if (aug_method == "random") { + mh_aug_result = metropolis_hastings_aug_ranking(as, rs.t(), n_items, R_obs.row(jj).t(), ar.t(), metric); + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + mh_aug_result = metropolis_hastings_aug_ranking_pseudo(as, rs.t(), n_items, R_obs.row(jj).t(), ar.t(), metric); + } + aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)) = mh_aug_result; + } + } + } + // return the history of the particles and their values + return Rcpp::List::create( + Rcpp::Named("rho_samples") = rho_samples, + Rcpp::Named("alpha_samples") = alpha_samples + ); +} diff --git a/src/smc_mallows_new_users_partial_alpha_fixed.cpp b/src/smc_mallows_new_users_partial_alpha_fixed.cpp new file mode 100644 index 00000000..54acc8f3 --- /dev/null +++ b/src/smc_mallows_new_users_partial_alpha_fixed.cpp @@ -0,0 +1,224 @@ +#include "RcppArmadillo.h" +#include "misc.h" +#include "smc.h" +#include "partitionfuns.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title SMC-mallows new users partial (alpha fixed) +//' @description Function to perform resample-move SMC algorithm where we receive new users with complete rankings +//' at each time step +//' @param R_obs Matrix containing the full set of observed rankings of size n_assessors by n_items +//' @param n_items Integer is the number of items in a ranking +//' @param metric A character string specifying the distance metric to use in the +//' Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @param leap_size leap_size Integer specifying the step size of the leap-and-shift +//' proposal distribution +//' @param N Integer specifying the number of particles +//' @param Time Integer specifying the number of time steps in the SMC algorithm +//' @param logz_estimate Estimate of the partition function, computed with +//' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. +//' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +//' @param num_new_obs Integer value for the number of new observations (complete rankings) for each time step +//' @param aug_method A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random" +//' @param alpha A numeric value of the scale parameter which is known and fixed +//' @return a set of particles each containing a value of rho and alpha +//' @export +// [[Rcpp::export]] +Rcpp::List smc_mallows_new_users_partial_alpha_fixed( + arma::mat& R_obs, + unsigned int& n_items, + std::string metric, + int& leap_size, + unsigned int& N, + unsigned int Time, + const Rcpp::Nullable logz_estimate, + int& mcmc_kernel_app, + unsigned int& num_new_obs, + std::string& aug_method, + double alpha +) { + + /* ====================================================== */ + /* Initialise Phase */ + /* ====================================================== */ + int n_users = R_obs.n_rows; // this is total- number of users + + // generate rho samples using uniform prior + arma::cube rho_samples(N, n_items, Time + 1, arma::fill::zeros); + for (arma::uword i = 0; i < N; ++i) { + arma::uvec items_sample = arma::randperm(n_items) + 1; + for (arma::uword j = 0; j < n_items; ++j) { + rho_samples(i, j, 0) = items_sample(j); + } + } + + // this is to store the augmentations of the observed rankings for each particle + arma::cube aug_rankings(n_users, n_items, N, arma::fill::zeros); // no. users by items by particles + + /* ====================================================== */ + /* New user situation */ + /* ====================================================== */ + unsigned int num_obs = 0; + + for (arma::uword tt = 0; tt < Time; ++tt) { + + /* ====================================================== */ + /* New Information */ + /* ====================================================== */ + // keep tally of how many ranking observations we have so far + num_obs = num_obs + num_new_obs; + + // create two ranking dataset to use for the reweight and move stages of the algorithm + // Note: + // new_observed_rankings = R_obs[((num_obs-num_new_obs+1):num_obs),] + // all_observed_rankings = R_obs[(1:num_obs),] + + // propagate particles onto the next time step + rho_samples.slice(tt + 1) = rho_samples.slice(tt); + + // calculate incremental weight and augmentation prob for each particle, + // based on new observed rankings + arma::vec log_inc_wgt(N, arma::fill::zeros); + + /* ====================================================== */ + /* Augment partial rankings */ + /* ====================================================== */ + + arma::ivec ranks = Rcpp::seq(1, n_items); + arma::vec aug_prob = Rcpp::rep(1.0, N); + + for (arma::uword ii = 0; ii < N; ++ii) { + for (arma::uword jj = num_obs - num_new_obs; jj < num_obs; ++jj) { + arma::vec partial_ranking = R_obs.row(jj).t(); + + // find items missing from original observed ranking + arma::uvec unranked_items = find_nonfinite(partial_ranking); + + // find ranks missing from ranking + Rcpp::NumericVector ranks_Cpp, partial_ranking_Cpp; + ranks_Cpp = ranks; + partial_ranking_Cpp = partial_ranking; + Rcpp::NumericVector missing_ranks = Rcpp::setdiff(ranks_Cpp, partial_ranking_Cpp); + + // fill in missing ranks based on choice of augmentation method + if (aug_method == "random") { + // create new agumented ranking by sampling remaining ranks from set uniformly + if (missing_ranks.length() == 1) { + partial_ranking.elem(arma::find_nonfinite(partial_ranking)) = Rcpp::as(missing_ranks); + } else { + partial_ranking.elem(arma::find_nonfinite(partial_ranking)) = Rcpp::as(Rcpp::sample(missing_ranks, missing_ranks.length())); + } + + aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)) = partial_ranking; + int missing_ranks_length = missing_ranks.length(); + int missing_ranks_length_fact = factorial(missing_ranks_length); // from misc.h + aug_prob(ii) = aug_prob(ii) * (1.0 / missing_ranks_length_fact); + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + // randomly permute the unranked items to give the order in which they will be allocated + arma::uvec item_ordering; + item_ordering = arma::conv_to::from(arma::shuffle(unranked_items)); + arma::rowvec rho_s = rho_samples(arma::span(ii), arma::span::all, arma::span(tt + 1)); + Rcpp::List proposal = calculate_forward_probability(\ + item_ordering, partial_ranking, missing_ranks, rho_s.t(),\ + alpha, n_items, metric\ + ); + arma::vec a_rank = proposal["aug_ranking"]; + double f_prob = proposal["forward_prob"]; + aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)) = a_rank; + aug_prob(ii) = aug_prob(ii) * f_prob; + } else { + Rcpp::stop("Combined choice of metric and aug_method is incompatible"); + } + } + } + + /* ====================================================== */ + /* Re-weight */ + /* ====================================================== */ + + for (arma::uword ii = 0; ii < N; ++ii) { + // evaluate the log estimate of the partition function for a particular + // value of alpha + + /* Initializing variables ------------------------------- */ + const Rcpp::Nullable cardinalities = R_NilValue; + arma::rowvec rho_samples_ii = \ + rho_samples(arma::span(ii), arma::span::all, arma::span(tt + 1)); + + /* Calculating log_z_alpha and log_likelihood ----------- */ + double log_z_alpha = get_partition_function(\ + n_items, alpha, cardinalities, logz_estimate, metric\ + ); + + arma::mat new_observed_rankings; + new_observed_rankings = aug_rankings(arma::span(num_obs - num_new_obs, num_obs - 1), arma::span::all, arma::span(ii)); + double log_likelihood = get_mallows_loglik(\ + alpha, rho_samples_ii.t(), n_items, new_observed_rankings, metric\ + ); + log_inc_wgt(ii) = log_likelihood - num_new_obs * log_z_alpha - log(aug_prob(ii)); + } + + /* normalise weights ------------------------------------ */ + double maxw = arma::max(log_inc_wgt); + arma::vec w = arma::exp(log_inc_wgt - maxw); + arma::vec norm_wgt = w / arma::sum(w); + + /* ====================================================== */ + /* Resample */ + /* ====================================================== */ + + /* Resample particles using multinomial resampling ------ */ + // Using norm_wgt_rcpp so that Rcpp::sample compiles. More details on + // https://github.com/ocbe-uio/BayesMallows/issues/90#issuecomment-866614296 + Rcpp::NumericVector norm_wgt_rcpp; + norm_wgt_rcpp = norm_wgt; + arma::uvec tt_vec, indices; + indices = Rcpp::as(Rcpp::sample(N, N, true, norm_wgt_rcpp)); + indices = indices - 1; + tt_vec = tt; + + /* Replacing tt + 1 slice on rho_samples ---------------- */ + arma::mat rho_samples_slice_11p1 = rho_samples.slice(tt + 1); + rho_samples_slice_11p1 = rho_samples_slice_11p1.rows(indices); + rho_samples.slice(tt + 1) = rho_samples_slice_11p1; + + /* Replacing tt + 1 column on alpha_samples ------------- */ + arma::cube aug_rankings_indices = aug_rankings.slices(indices); + aug_rankings.rows(0, num_obs - 1) = aug_rankings_indices(arma::span(0, num_obs - 1), arma::span::all, arma::span::all); + + /* ====================================================== */ + /* Move step */ + /* ====================================================== */ + for (arma::uword ii = 0; ii < N; ++ii) { + arma::mat all_observed_rankings; + all_observed_rankings = aug_rankings(arma::span(0, num_obs - 1), arma::span::all, arma::span(ii)); + arma::mat rs_slice = rho_samples.slice(tt + 1); + arma::rowvec rs = rs_slice.row(ii); + // move each particle containing sample of rho and alpha by using + // the MCMC kernels + rho_samples(arma::span(ii), arma::span::all, arma::span(tt + 1)) =\ + metropolis_hastings_rho(\ + alpha, n_items, all_observed_rankings, metric, rs.t(), leap_size\ + ); + for (arma::uword jj = 0; jj < num_obs; ++jj) { + arma::rowvec ar; + ar = aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)); + arma::vec mh_aug_result; + if (aug_method == "random") { + mh_aug_result = metropolis_hastings_aug_ranking(\ + alpha, rs.t(), n_items, R_obs.row(jj).t(), ar.t(), metric\ + ); + } else if ((aug_method == "pseudolikelihood") & ((metric == "footrule") | (metric == "spearman"))) { + mh_aug_result = metropolis_hastings_aug_ranking_pseudo( + alpha, rs.t(), n_items, R_obs.row(jj).t(), ar.t(), metric\ + ); + } + aug_rankings(arma::span(jj), arma::span::all, arma::span(ii)) = mh_aug_result; + } + } + } + // return the history of the particles and their values + return Rcpp::List::create((Rcpp::Named("rho_samples") = rho_samples)); +} diff --git a/src/smc_metropolis_hastings_alpha.cpp b/src/smc_metropolis_hastings_alpha.cpp new file mode 100644 index 00000000..3fe15765 --- /dev/null +++ b/src/smc_metropolis_hastings_alpha.cpp @@ -0,0 +1,78 @@ +#include "RcppArmadillo.h" +#include "smc.h" +#include "partitionfuns.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Metropolis-Hastings Alpha +//' @description Function to perform Metropolis-Hastings for new rho under +//' the Mallows model with footrule distance metric! +//' @param alpha Numeric value og the scale parameter +//' @param n_items Integer is the number of items in a ranking +//' @param rankings the observed rankings, i.e, preference data +//' @details \code{rankings} is a matrix of size +//' \eqn{N }\eqn{\times}{x}\eqn{ n_items} of rankings in each row. +//' Alternatively, if \eqn{N} equals 1, \code{rankings} can be a vector. +//' @param metric A character string specifying the distance metric to use +//' in the Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, +//' and \code{"ulam"}. +//' @param rho Numeric vector specifying the current consensus ranking +//' @param logz_estimate Estimate grid of log of partition function, +//' computed with \code{\link{estimate_partition_function}} in +//' the BayesMallow R package {estimate_partition_function}. +//' @param alpha_prop_sd Numeric value specifying the standard deviation of the +//' lognormal proposal distribution used for \eqn{\alpha} in the +//' Metropolis-Hastings algorithm. Defaults to \code{0.1}. +//' @return \code{alpha} or \code{alpha_prime}: Numeric value to be used +//' as the proposal of a new alpha +//' @param lambda Strictly positive numeric value specifying the rate parameter +//' of the truncated exponential prior distribution of \eqn{\alpha}. Defaults +//' to \code{0.1}. When \code{n_cluster > 1}, each mixture component +//' \eqn{\alpha_{c}} has the same prior distribution. +//' @param alpha_max Maximum value of \code{alpha} in the truncated exponential +//' prior distribution. +//' @importFrom stats dexp rlnorm runif +//' @author Anja Stein +//' @example /inst/examples/metropolis_hastings_alpha_example.R +//' +//' @export +// [[Rcpp::export]] +double metropolis_hastings_alpha( + double alpha, + int n_items, + arma::mat rankings, + std::string metric, + arma::vec rho, + const Rcpp::Nullable logz_estimate, + double alpha_prop_sd, + double lambda, + double alpha_max +) { + double rand = Rcpp::as(Rcpp::rnorm(1, 0, 1)); + double alpha_prime_log = rand * alpha_prop_sd + std::log(alpha); + double alpha_prime = std::exp(alpha_prime_log); + + // Difference between current and proposed alpha + double alpha_diff = alpha - alpha_prime; + double mallows_loglik_prop = get_mallows_loglik(alpha_prime - alpha, rho, n_items, rankings, metric); + + // evaluate the log estimate of the partition function for a particular + // value of alpha + const Rcpp::Nullable cardinalities = R_NilValue; + double logz_alpha = get_partition_function(n_items, alpha, cardinalities, logz_estimate, metric); + double logz_alpha_prime = get_partition_function(n_items, alpha_prime, cardinalities, logz_estimate, metric); + + double obs_freq = rankings.n_elem / n_items; + + // Compute the Metropolis-Hastings ratio + double loga = mallows_loglik_prop + lambda * alpha_diff + obs_freq * (logz_alpha - logz_alpha_prime) + log(alpha_prime) - log(alpha); + + // determine whether to accept or reject proposed rho and return now consensus + // ranking + double p = Rcpp::as(Rcpp::runif(1, 0, 1)); + if (log(p) <= loga && alpha_prime < alpha_max) { + return(alpha_prime); + } else { + return(alpha); + } +} \ No newline at end of file diff --git a/src/smc_metropolis_hastings_aug_ranking.cpp b/src/smc_metropolis_hastings_aug_ranking.cpp new file mode 100644 index 00000000..b0ada3d6 --- /dev/null +++ b/src/smc_metropolis_hastings_aug_ranking.cpp @@ -0,0 +1,82 @@ +#include "RcppArmadillo.h" +#include "smc.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Metropolis-Hastings Augmented Ranking +//' @description Function to perform Metropolis-Hastings for new augmented ranking +//' +//' @param alpha Numeric value og the scale parameter +//' @param rho Numeric vector specifying the consensus ranking +//' @param n_items Integer is the number of items in a ranking +//' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs +//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +//' @param metric A character string specifying the distance metric to use in the +//' Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @return R_curr or R_obs A ranking sequence vector representing proposed augmented ranking for next iteration of MCMC chain +//' @export +// [[Rcpp::export]] +arma::vec metropolis_hastings_aug_ranking( + double alpha, + arma::vec rho, + int n_items, + arma::vec partial_ranking, + arma::vec current_ranking, + std::string metric +) { + // augment incomplete ranks to initialise + arma::vec ranks; + Rcpp::IntegerVector tmp = Rcpp::seq(1, n_items); + ranks = Rcpp::as(tmp); + + // find items missing from original observed ranking + arma::uvec unranked_items = find_nonfinite(partial_ranking); + + // find unallocated ranks from original observed ranking + Rcpp::NumericVector p_rank_Rcpp, c_rank_Rcpp; + p_rank_Rcpp = partial_ranking; + c_rank_Rcpp = current_ranking; + arma::vec remaining_set = Rcpp::sort_unique(Rcpp::setdiff(c_rank_Rcpp, p_rank_Rcpp)); + + + // if the observed and augmented ranking are exactly the same then break + bool condition_1 = arma::approx_equal(\ + partial_ranking, current_ranking, "absdiff", 0.1\ + ); + bool condition_2 = remaining_set.n_elem == 1; + if (condition_1 | condition_2) { + return(current_ranking); + } else { + + // generate random order for remaining_set + arma::vec A = arma::shuffle(remaining_set); + remaining_set = std::move(A); + + // Subset by element position and set equal to the now permuted remaining set + partial_ranking.elem(unranked_items) = remaining_set; + + // set the augmented partial ranking as the proposed augmented ranking + arma::vec proposed_ranking = partial_ranking; + + + /* MH TIME ------------------------------------------------------------- */ + // Calculate the log posterior of the current and proposed rankings. + // NB the current can usually be stored to save recalculating it, but we're not caring about that yet + double curr_logpost = get_mallows_loglik(\ + alpha, rho, n_items, current_ranking.t(), metric\ + ); + double prop_logpost = get_mallows_loglik(\ + alpha, rho, n_items, proposed_ranking.t(), metric\ + ); + + double log_acceptance_prob = prop_logpost - curr_logpost; + + if (std::log(Rcpp::as(Rcpp::runif(1, 0, 1))) < log_acceptance_prob) { + return(proposed_ranking); + } else { + return(current_ranking); + } + + } +} diff --git a/src/smc_metropolis_hastings_aug_ranking_pseudo.cpp b/src/smc_metropolis_hastings_aug_ranking_pseudo.cpp new file mode 100644 index 00000000..44a8ebf0 --- /dev/null +++ b/src/smc_metropolis_hastings_aug_ranking_pseudo.cpp @@ -0,0 +1,93 @@ +#include "RcppArmadillo.h" +#include "smc.h" + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Metropolis-Hastings Augmented Ranking (pseudolikelihood) +//' @description Function to perform Metropolis-Hastings for new augmented ranking using the pseudolikelihood augmentation approach +//' +//' @param alpha Numeric value og the scale parameter +//' @param rho Numeric vector specifying the consensus ranking +//' @param n_items Integer is the number of items in a ranking +//' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs +//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +//' @param metric A character string specifying the distance metric to use in the +//' Bayesian Mallows Model. Available options are \code{"footrule"}, +//' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and +//' \code{"ulam"}. +//' @return = proposed augmented ranking or current ranking A ranking sequence vector representing proposed augmented ranking for next +//' iteration of MCMC chain +//' @export +// [[Rcpp::export]] + +arma::vec metropolis_hastings_aug_ranking_pseudo( + double alpha, + arma::vec rho, + int n_items, + arma::vec partial_ranking, + arma::vec current_ranking, + std::string metric +) { + // augment incomplete ranks to initialise + arma::vec ranks; + Rcpp::IntegerVector tmp = Rcpp::seq(1, n_items); + ranks = Rcpp::as(tmp); + + // find items missing from original observed ranking + arma::uvec unranked_items = find_nonfinite(partial_ranking); + + // find unallocated ranks from original observed ranking + Rcpp::NumericVector p_rank_Rcpp, c_rank_Rcpp; + p_rank_Rcpp = partial_ranking; + c_rank_Rcpp = current_ranking; + arma::vec remaining_set = Rcpp::setdiff(c_rank_Rcpp, p_rank_Rcpp); + + // if the observed and augmented ranking are exactly the same then break + bool condition_1 = arma::approx_equal(\ + partial_ranking, current_ranking, "absdiff", 0.1\ + ); + bool condition_2 = remaining_set.n_elem == 1; + if (condition_1 | condition_2) { + return(current_ranking); + } else { + // randomly permute the unranked items to give the order in which they will + // be allocated + Rcpp::IntegerVector unranked_items_Rcpp; + unranked_items_Rcpp = arma::conv_to::from(unranked_items); + arma::uvec item_ordering = Rcpp::as(\ + Rcpp::sample(unranked_items_Rcpp, unranked_items_Rcpp.length())\ + ); + item_ordering = item_ordering + 1; + + // Calculate probabilities + Rcpp::List proposal = calculate_forward_probability(\ + item_ordering, partial_ranking, remaining_set, rho, alpha, n_items,\ + metric\ + ); + arma::vec proposed_augmented_ranking = proposal["aug_ranking"]; + double forward_prob = proposal["forward_prob"]; + + double backward_prob = calculate_backward_probability(\ + item_ordering, partial_ranking, current_ranking, remaining_set, rho,\ + alpha, n_items, metric\ + ); + + /* MH TIME ------------------------------------------------------------- */ + // Calculate the log posterior of the current and proposed rankings. + // NB the current can usually be stored to save recalculating it, but we're not caring about that yet + double curr_logpost = get_mallows_loglik(\ + alpha, rho, n_items, current_ranking.t(), metric\ + ); + double prop_logpost = get_mallows_loglik(\ + alpha, rho, n_items, proposed_augmented_ranking.t(), metric\ + ); + + double log_acceptance_prob = prop_logpost - curr_logpost - std::log(forward_prob) + std::log(backward_prob); + + if (std::log(Rcpp::as(Rcpp::runif(1, 0, 1))) < log_acceptance_prob) { + return(proposed_augmented_ranking); + } else { + return(current_ranking); + } + + } +} diff --git a/src/smc_metropolis_hastings_rho.cpp b/src/smc_metropolis_hastings_rho.cpp new file mode 100644 index 00000000..40b25d9e --- /dev/null +++ b/src/smc_metropolis_hastings_rho.cpp @@ -0,0 +1,73 @@ +#include "RcppArmadillo.h" +#include "smc.h" +#include + +// [[Rcpp::depends(RcppArmadillo)]] +//' @title Metropolis-Hastings Rho +//' @description Function to perform Metropolis-Hastings for new rho under the Mallows model with footrule distance metric! +//' @inheritParams get_mallows_loglik +//' @param leap_size Integer specifying the step size of the leap-and-shift +//' proposal distribution. +//' @export +//' @author Anja Stein +//' @examples +//' rho <- t(c(1,2,3,4,5,6)) +//' alpha <- 2 +//' metric <- "footrule" +//' n_items <- 6 +//' +//' metropolis_hastings_rho( +//' alpha = alpha, n_items = n_items, rankings = rho, metric = metric, +//' rho = rho, leap_size = 1 +//' ) +//' +//' metropolis_hastings_rho( +//' alpha = alpha, n_items = n_items, rankings = rho, metric = metric, +//' rho = rho, leap_size = 2 +//' ) +//' +//' metropolis_hastings_rho( +//' alpha = alpha, n_items = n_items, rankings = rho, metric = metric, +//' rho = rho, leap_size = 3 +//' ) +//' +//' rankings <- sample_mallows( +//' rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 +//' ) +//' metropolis_hastings_rho( +//' alpha = alpha, n_items = n_items, rankings = rankings, metric = metric, +//' rho = rho, leap_size = 1 +//' ) +//' +// [[Rcpp::export]] +arma::vec metropolis_hastings_rho( + double alpha, + int n_items, + arma::mat rankings, + std::string metric, + arma::vec rho, + int leap_size +) { + // create new potential consensus ranking + Rcpp::List kernel = leap_and_shift_probs(rho, leap_size, n_items); + + // output from leap-and-shift is of the following + arma::vec rho_prime = Rcpp::as(kernel["rho_prime"]); + double forwards_prob = Rcpp::as(kernel["forwards_prob"]); // rho_prime|rho + double backwards_prob = Rcpp::as(kernel["backwards_prob"]); // rho|rho_prime + + // evaluate the log-likelihood with current rankings + double mallows_loglik_curr = get_mallows_loglik(alpha, rho, n_items, rankings, metric); + double mallows_loglik_prop = get_mallows_loglik(alpha, rho_prime, n_items, rankings, metric); + + // calculate acceptance probability + double loga = std::log(backwards_prob) - std::log(forwards_prob) + mallows_loglik_prop - mallows_loglik_curr; + + // determine whether to accept or reject proposed rho and return now consensus ranking + double p = Rcpp::as(Rcpp::runif(1, 0, 1)); + if (std::log(p) <= loga) { + return(rho_prime); + } else { + return(rho); + } +} diff --git a/tests/testthat/test_assess_convergence.R b/tests/testthat/test-assess_convergence.R similarity index 100% rename from tests/testthat/test_assess_convergence.R rename to tests/testthat/test-assess_convergence.R diff --git a/tests/testthat/test-bulletproofing.R b/tests/testthat/test-bulletproofing.R new file mode 100644 index 00000000..e3cce501 --- /dev/null +++ b/tests/testthat/test-bulletproofing.R @@ -0,0 +1,104 @@ +context("SMC bulletproofing") + +limit <- sushi_rankings +n_items <- dim(limit)[2] +n_users <- dim(limit)[1] +metric <- "footrule" + +# Generate estimate of Z_n(alpha) +# Estimate the logarithm of the partition function of the Mallows rank model. +# We create a grid of alpha values from 0 to 10 +alpha_vector <- seq(from = 0, to = 20, by = 0.1) +iter <- 1e1 +degree <- 10 + +# Estimate the logarithm of the partition function of the Mallows rank model using the estimate partition function +logz_estimate <- estimate_partition_function( + "importance_sampling", alpha_vector, n_items, metric, iter, degree +) +leap_size <- floor(n_items / 5) +rho_init <- c(1, 8, 6, 3, 9, 2, 5, 7, 4, 10) +alpha_init <- 5 +alpha_prop_sd <- 0.5 +lambda <- 0.1 +alpha_max <- 20 +cluster_rankings <- sushi_rankings[1, ] + +test_that("Functions accept rankings and rho as row or column vectors", { + mhr_r_r <- metropolis_hastings_rho( + alpha_init, n_items, + rankings = as.matrix(cluster_rankings), + metric, + rho = rho_init, + leap_size + ) + mha_r_r <- metropolis_hastings_alpha( + alpha_init, n_items, + rankings = as.matrix(cluster_rankings), + metric, + rho = rho_init, + logz_estimate, + alpha_prop_sd, + lambda, + alpha_max + ) + mhr_rt_r <- metropolis_hastings_rho( + alpha_init, n_items, + rankings = t(cluster_rankings), + metric, + rho = rho_init, + leap_size + ) + mha_rt_r <- metropolis_hastings_alpha( + alpha_init, n_items, + rankings = t(cluster_rankings), + metric, + rho = rho_init, + logz_estimate, + alpha_prop_sd, + lambda, + alpha_max + ) + mhr_r_rt <- metropolis_hastings_rho( + alpha_init, n_items, + rankings = as.matrix(cluster_rankings), + metric, + rho = t(rho_init), + leap_size + ) + mha_r_rt <- metropolis_hastings_alpha( + alpha_init, n_items, + rankings = as.matrix(cluster_rankings), + metric, + rho = t(rho_init), + logz_estimate, + alpha_prop_sd, + lambda, + alpha_max + ) + mhr_rt_rt <- metropolis_hastings_rho( + alpha_init, n_items, + rankings = t(cluster_rankings), + metric, + rho = t(rho_init), + leap_size + ) + mha_rt_rt <- metropolis_hastings_alpha( + alpha_init, n_items, + rankings = t(cluster_rankings), + metric, + rho = t(rho_init), + logz_estimate, + alpha_prop_sd, + lambda, + alpha_max + ) + expect_equal(dim(mhr_r_r), c(10, 1)) + expect_length(mha_r_r, 1) + expect_equal(dim(mhr_rt_r), c(10, 1)) + expect_length(mha_rt_r, 1) + expect_equal(dim(mhr_r_rt), c(10, 1)) + expect_length(mha_r_rt, 1) + expect_equal(dim(mhr_rt_rt), c(10, 1)) + expect_length(mha_rt_rt, 1) +}) diff --git a/tests/testthat/test_compute_consensus.R b/tests/testthat/test-compute_consensus.R similarity index 100% rename from tests/testthat/test_compute_consensus.R rename to tests/testthat/test-compute_consensus.R diff --git a/tests/testthat/test_compute_mallows.R b/tests/testthat/test-compute_mallows.R similarity index 100% rename from tests/testthat/test_compute_mallows.R rename to tests/testthat/test-compute_mallows.R diff --git a/tests/testthat/test_distance_function.R b/tests/testthat/test-distance_function.R similarity index 100% rename from tests/testthat/test_distance_function.R rename to tests/testthat/test-distance_function.R diff --git a/tests/testthat/test_estimate_partition_function.R b/tests/testthat/test-estimate_partition_function.R similarity index 100% rename from tests/testthat/test_estimate_partition_function.R rename to tests/testthat/test-estimate_partition_function.R diff --git a/tests/testthat/test_expected_dist.R b/tests/testthat/test-expected_dist.R similarity index 100% rename from tests/testthat/test_expected_dist.R rename to tests/testthat/test-expected_dist.R diff --git a/tests/testthat/test_generate_ranking.R b/tests/testthat/test-generate_ranking.R similarity index 100% rename from tests/testthat/test_generate_ranking.R rename to tests/testthat/test-generate_ranking.R diff --git a/tests/testthat/test_lik_db_mix.R b/tests/testthat/test-lik_db_mix.R similarity index 100% rename from tests/testthat/test_lik_db_mix.R rename to tests/testthat/test-lik_db_mix.R diff --git a/tests/testthat/test_mcmc_function.R b/tests/testthat/test-mcmc_function.R similarity index 100% rename from tests/testthat/test_mcmc_function.R rename to tests/testthat/test-mcmc_function.R diff --git a/tests/testthat/test_misc_cpp.R b/tests/testthat/test-misc_cpp.R similarity index 100% rename from tests/testthat/test_misc_cpp.R rename to tests/testthat/test-misc_cpp.R diff --git a/tests/testthat/test_misc_functions.R b/tests/testthat/test-misc_functions.R similarity index 100% rename from tests/testthat/test_misc_functions.R rename to tests/testthat/test-misc_functions.R diff --git a/tests/testthat/test_partition_function.R b/tests/testthat/test-partition_function.R similarity index 100% rename from tests/testthat/test_partition_function.R rename to tests/testthat/test-partition_function.R diff --git a/tests/testthat/test_plot.R b/tests/testthat/test-plot.R similarity index 100% rename from tests/testthat/test_plot.R rename to tests/testthat/test-plot.R diff --git a/tests/testthat/test_plot_elbow.R b/tests/testthat/test-plot_elbow.R similarity index 100% rename from tests/testthat/test_plot_elbow.R rename to tests/testthat/test-plot_elbow.R diff --git a/tests/testthat/test_plot_top_k.R b/tests/testthat/test-plot_top_k.R similarity index 100% rename from tests/testthat/test_plot_top_k.R rename to tests/testthat/test-plot_top_k.R diff --git a/tests/testthat/test-print.R b/tests/testthat/test-print.R index ed64e0b8..0aff5bd8 100644 --- a/tests/testthat/test-print.R +++ b/tests/testthat/test-print.R @@ -1,4 +1,27 @@ -test_that("print function works", { - m <- compute_mallows(potato_visual, nmc = 3) - expect_output(print(m), "Bayesian Mallows Model with 20 items and 12 assessors.") +context("Testing print.BayesMallows and print.BayesMallowsMixtures") + +test_that("print.BayesMallows fails when it should", { + class(mtcars) <- "BayesMallows" + expect_error(print(mtcars)) + m <- compute_mallows(potato_visual, nmc = 5) + m$n_items <- NULL + expect_error(print(m), "BayesMallows object must have elements n_items and n_assessors.") + m$n_assessors <- NULL + expect_error(print(m), + "BayesMallows object must have elements n_items and n_assessors.") +}) + + +test_that("print.BayesMallowsMixtures fails when it should", { + class(mtcars) <- "BayesMallowsMixtures" + expect_error(print(mtcars), + regexp = "All elements of a BayesMallowsMixtures object must be of class BayesMallows.") + m <- compute_mallows_mixtures(potato_visual, n_clusters = 1:3, nmc = 5) + class(m[[1]]) <- "list" + expect_error(print(m), + regexp = "All elements of a BayesMallowsMixtures object must be of class BayesMallows.") + + m <- compute_mallows_mixtures(potato_visual, n_clusters = 1:3, nmc = 5) + expect_output(print(m), + "Collection of 3 Bayesian Mallows Mixture Models with the following number of mixture components:") }) diff --git a/tests/testthat/test_random_number_seed.R b/tests/testthat/test-random_number_seed.R similarity index 100% rename from tests/testthat/test_random_number_seed.R rename to tests/testthat/test-random_number_seed.R diff --git a/tests/testthat/test_rank_conversion.R b/tests/testthat/test-rank_conversion.R similarity index 100% rename from tests/testthat/test_rank_conversion.R rename to tests/testthat/test-rank_conversion.R diff --git a/tests/testthat/test_rank_freq_distr.R b/tests/testthat/test-rank_freq_distr.R similarity index 100% rename from tests/testthat/test_rank_freq_distr.R rename to tests/testthat/test-rank_freq_distr.R diff --git a/tests/testthat/test_sample_mallows.R b/tests/testthat/test-sample_mallows.R similarity index 100% rename from tests/testthat/test_sample_mallows.R rename to tests/testthat/test-sample_mallows.R diff --git a/tests/testthat/test-smc_individual_functions.R b/tests/testthat/test-smc_individual_functions.R new file mode 100644 index 00000000..ea63a153 --- /dev/null +++ b/tests/testthat/test-smc_individual_functions.R @@ -0,0 +1,208 @@ +context("SMC individual functions") + +rho <- c(1,2,3,4,5,6) +alpha <- 2 +metric <- "footrule" +n_items <- 6 + +test_that("get_mallows_loglik() works as expected", { + set.seed(101) + loglik <- get_mallows_loglik( + alpha = alpha, rho = t(rho), n_items = length(rho), rankings = t(rho), + metric = metric + ) + expect_equal(loglik, 0) + + rankings <- sample_mallows( + rho0 = rho, alpha0 = alpha, n_samples = 10, + burnin = 1000, thinning = 500 + ) + loglik <- get_mallows_loglik( + alpha = alpha, rho = rho, n_items = n_items, rankings = rankings, + metric = metric + ) + expect_equivalent(loglik, -22.6667, tol=1e-4) +}) + +test_that("smc_metropolis_hastings_rho() works as expected", { + set.seed(101) + # This functions uses get_mallows_log_lik and leap_and_shift_probs + # so if the checks match in those worker functions then it is very likely + # that this function will return the correct outputs. + rankings <- sample_mallows( + rho0 = rho, alpha0 = alpha, n_samples = 10, + burnin = 1000, thinning = 500 + ) + + # you can confirm the print statements inside the metropolis_hastings_rho + # match get_mallows_loglik and leap_and_shift_probs + test_1 <- metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, + rho = rho, leap_size = 1 + ) + dist_1 <- BayesMallows:::get_rank_distance(rho, test_1, metric= "ulam") + expect_equal(test_1, as.matrix(c(1, 2, 3, 5, 4, 6))) + # if rho != rho_prime, then it should have a ulam distance of 1 + # if rho == rho_prime, then it should have ulam distance of 0 + expect_equal(dist_1, 1) + + test_2 <- metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, + rho = rho, leap_size = 2 + ) + dist_2 <- BayesMallows:::get_rank_distance(rho, test_2, metric = "ulam") + expect_equal(test_2, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(dist_2, 0) + + test_3 <- metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, + rho = rho, leap_size = 3 + ) + dist_3 <- BayesMallows:::get_rank_distance(rho, test_3, metric = "ulam") + expect_equal(test_3, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(dist_3, 0) + + # we have a ranking data set containing 10 rankings over 6 items + test_4 <- metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = rankings, metric = metric, + rho = rho, leap_size = 1 + ) + dist_4 <- BayesMallows:::get_rank_distance(rho, test_4, metric = "ulam") + expect_equal(test_4, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(dist_4, 0) +}) + +test_that("smc_leap_and_shift_probs() works as expected", { + set.seed(101) + n_items <- length(rho) + + # leap_size has a possible range, the BayesMallows papers suggest + # leap_size = floor(n_items/5) but the leap_size can be up to n_items/2. + # Note that leap_size must be integered valued. + + # if leap_size = 1, then forwards_prob = backwards_prob + test_1 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 1) + expect_equal(test_1$rho_prime, as.matrix(c(1, 3, 2, 4, 5, 6))) + expect_equivalent(test_1$forwards_prob, 0.1666667, tol=1e-6) + expect_equivalent(test_1$backwards_prob, 0.1666667, tol=1e-6) + + # if rho != rho_prime, then it should have a ulam distance of 1 + # if rho == rho_prime, then it should have ulam distance of 0 + dist_1 <- BayesMallows:::get_rank_distance(rho, test_1$rho_prime, metric= "ulam") + expect_equal(dist_1, 1) + + test_2 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 2) + expect_equal(test_2$rho_prime, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equivalent(test_2$forwards_prob, 0.0556, tol=1e-4) + expect_equivalent(test_2$backwards_prob, 0.0556, tol=1e-4) + + dist_2 <- get_rank_distance( + rho, test_2$rho_prime, metric= "ulam" + ) + expect_equal(dist_2, 0) + + test_3 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 3) + expect_equal(test_3$rho_prime, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equivalent(test_3$forwards_prob, 0.0417, tol=1e-3) + expect_equivalent(test_3$backwards_prob, 0.0417, tol=1e-3) + + dist_3 <- get_rank_distance( + rho, test_3$rho_prime, metric= "ulam" + ) + expect_equal(dist_3, 0) +}) + +# ======================================================== # +# unit test for metropolis_hastings_alpha.R # +# ======================================================== # + +metropolis_hastings_alpha_old <- function( + alpha, n_items, rankings, metric, rho, logz_estimate +) { + exp_alpha_prime <- rlnorm(1, mean = alpha, sd = 0.15) # 1 + alpha_prime <- log(exp_alpha_prime) + + # evaluate the log-likelihood with current rankings + mallows_loglik_prop <- get_mallows_loglik( + alpha = (alpha_prime - alpha), rho = rho, n = n_items, + rankings = rankings, metric = metric + ) + + # evaluate the log estimate of the partition function + # for a particular value of alpha + logz_alpha <- get_partition_function( + n_items = n_items, alpha = alpha, logz_estimate = logz_estimate, + metric = metric + ) + logz_alpha_prime <- get_partition_function( + n_items = n_items, alpha = alpha_prime, logz_estimate = logz_estimate, + metric = metric + ) + + n_users <- length(rankings) / n_items + + loga <- n_users * (logz_alpha - logz_alpha_prime) + + dexp(alpha_prime, log=TRUE) - dexp(alpha, log=TRUE) + + alpha_prime - alpha + mallows_loglik_prop + + # determine whether to accept or reject proposed rho and + # return now consensus ranking + p <- runif(1, min = 0, max = 1) + if (log(p) <= loga) { + return(alpha_prime) + } else { + return(alpha) + } +} + +set.seed(101) +rho <- c(1,2,3,4,5,6) +alpha <- 2 +metric <- "footrule" +n_items <- 6 +rankings <- sample_mallows( + rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 +) +alpha_vector <- seq(from = 0, to = 20, by = 1) +iter <- 1e4 +degree <- 10 + +# Estimate the logarithm of the partition function of the Mallows rank model +# using the estimate partition function +logz_estimate <- estimate_partition_function( + method = "importance_sampling", alpha_vector = alpha_vector, + n_items = n_items, metric = "footrule", nmc = iter, degree = degree +) +set.seed(101) +test_1_a <- metropolis_hastings_alpha_old(alpha, n_items, rankings, metric, rho, logz_estimate) +test_1_b <- metropolis_hastings_alpha( + alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd = 0.5, + lambda = 0.1, alpha_max = 20 +) +set.seed(101) +test_2_a <- metropolis_hastings_alpha_old( + alpha, n_items, rankings, metric, rho, logz_estimate +) +test_2_b <- metropolis_hastings_alpha( + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.1, alpha_max = 20 +) +set.seed(101) +test_3_b <- metropolis_hastings_alpha( + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 20 +) +set.seed(101) +test_4_b <- metropolis_hastings_alpha( + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.15, alpha_max = 20 +) + +test_that("metropolis_hastings_alpha() works as expected", { + expect_equivalent(test_1_a, 1.951095, tol=1e-5) + expect_equivalent(test_1_b, 2.450351, tol=1e-5) + expect_equivalent(test_2_a, 1.951095, tol=1e-5) + expect_equivalent(test_2_b, 2.125639, tol=1e-5) + expect_equivalent(test_3_b, 2) + expect_equivalent(test_4_b, 1.904542, tol=1e-5) +}) diff --git a/tests/testthat/test-smc_mallows_complete_rankings.R b/tests/testthat/test-smc_mallows_complete_rankings.R new file mode 100755 index 00000000..e784fc9d --- /dev/null +++ b/tests/testthat/test-smc_mallows_complete_rankings.R @@ -0,0 +1,180 @@ +context("SMC complete rankings: sequence") + +######################### +# Generate Dataset +######################### +set.seed(994) + +data = sushi_rankings[1:100,] + +# General +n_items <- dim(sushi_rankings)[2] # Number of items +leap_size = floor(n_items/5) +metric = "footrule" + +# Generate estimate of Z_n(alpha) +alpha_vector <- seq(from = 0, to = 15, by = 1) +iter = 1e2 +degree <- 10 + +# Estimate the logarithm of the partition function of the Mallows rank model using the estimate partition function +logz_estimate <- estimate_partition_function(method = "importance_sampling", + alpha_vector = alpha_vector, + n_items = n_items, metric = metric, + nmc = iter, degree = degree) + + +###################################### +# BayesMallows Analysis (MCMC) +###################################### +nmc = 20 +burnin=5 +model_fit <- compute_mallows(rankings = data, nmc = nmc, metric = metric, leap_size =leap_size, + alpha_prop_sd = 0.15, logz_estimate = logz_estimate) + +model_fit$burnin = burnin + +alpha_samples_table = data.frame(iteration = 1:nmc , value = model_fit$alpha$value) +alpha_samples_table = alpha_samples_table[(burnin+1):nmc,] + +# from observing the plots, this looks like the estimated parameters of the Mallows Model +rho_0 = c(4,5,2,6,8,3,9,1,7,10) +alpha_0 = 1.7 + +# heatplot - there is no burnin! +mcmc_rho_matrix = matrix(model_fit$rho$value, ncol = n_items, nrow = nmc, byrow=TRUE) + +# ################################################################### +# # SMC +# ################################################################### +mcmc_times = 5 +num_new_obs = 10 +Time = dim(data)[1]/num_new_obs +N = 100 + +test <- smc_mallows_new_users_complete( + R_obs = data, n_items = n_items, metric = metric, + leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_times, + alpha_prop_sd = 0.1, lambda = 0.001, alpha_max = 1e6, + num_new_obs = num_new_obs, verbose = FALSE +) + +test_that("Output of smc_mallows_new_users_complete is OK", { + expect_s3_class(test, "SMCMallows") + expect_length(test, 2) + expect_named(test, c("rho_samples", "alpha_samples")) + expect_equal(dim(test$rho_samples), c(100, 10, 111)) + expect_equal(dim(test$alpha_samples), c(100, 111)) +}) + +# ############################### +# # Analysis +# ############################### + +# posterior confidence intervals for rho +rho_temp <- compute_posterior_intervals_rho( + output = test$rho_samples[,,Time+1], nmc = N, burnin = 0 +) + +# MAP AND CP consensus ranking estimates +rho_cp <- compute_rho_consensus( + output = test$rho_samples[,,Time+1], nmc = N, burnin = 0, C = 1, type = "CP" +) +rho_map <- compute_rho_consensus(output = test$rho_samples[,,Time+1], nmc = N, burnin = 0, C = 1, type = "MAP") + +test_that("Output of compute_posterior_intervals_rho is OK", { + expect_is(rho_temp, "tbl_df") + expect_length(rho_temp, 7) + expect_named( + rho_temp, + c( + "item", "parameter", "mean", "median", "conf_level", "hpdi", + "central_interval" + ) + ) + expect_equivalent(sapply(rho_temp, length), rep(10, 7)) +}) + +# posterior for alpha +alpha_samples_table = data.frame( + iteration = 1:N , value = test$alpha_samples[,Time+1] +) +# posterior confidence intervals +alpha_posterior_intervals = compute_posterior_intervals_alpha( + output = test$alpha_samples[,Time+1], nmc = N, burnin = 0 +) + +test_that("Output of compute_posterior_intervals_alpha is OK", { + expect_is(alpha_posterior_intervals, "tbl_df") + expect_length(alpha_posterior_intervals, 6) + expect_named( + alpha_posterior_intervals, + c( + "parameter", "mean", "median", "conf_level", "hpdi", + "central_interval" + ) + ) + expect_equivalent(sapply(alpha_posterior_intervals, length), rep(1, 6)) +}) + +context("SMC complete rankings: breakdown") + +test_that("get_mallows_loglik() in smc_mallows_new_users_complete() works", { + # ======================================================== # + # Setup # + # ======================================================== # + + # Basic elements ----------------------------------------- # + data <- sushi_rankings[1:100, ] + n_users <- nrow(data) + n_items <- ncol(sushi_rankings) + Time <- nrow(data) / num_new_obs + num_new_obs <- 10 + N <- 100 + + # rho_samples and alpha_samples -------------------------- # + rho_samples <- array(data=0, dim=c(N, n_items, (n_users + Time + 1))) + for (ii in seq_len(N)){ + rho_samples[ii, , 1] <- sample(seq_len(n_items), n_items, replace=FALSE) + } + alpha_samples <- matrix(nrow=N, ncol=(n_items + Time + 1)) + alpha_samples[, 1] <- rexp(N, rate=1) + + # logz_estimate ------------------------------------------ # + alpha_vector <- seq(from = 0, to = 15, by = 1) + iter <- 3e2 + degree <- 10 + logz_estimate <- estimate_partition_function( + method="importance_sampling", alpha_vector=alpha_vector, + n_items=n_items, metric=metric, nmc=iter, degree=degree + ) + + num_obs <- 0 + out_loglik <- vector(mode="numeric", length=Time) + for (tt in seq_len(Time)) { + num_obs <- num_obs + num_new_obs + new_observed_rankings <- data[(num_obs - num_new_obs + 1):num_obs, ] + rho_samples[, , tt + 1] <- rho_samples[, , tt] + alpha_samples[, tt + 1] <- alpha_samples[, tt] + alpha_samples_ii <- alpha_samples[ii, tt + 1] + rho_samples_ii <- rho_samples[ii, , tt + 1] + for (ii in seq_len(N)) { + log_z_alpha <- BayesMallows:::get_partition_function( + n_items, alpha_samples_ii, NULL, logz_estimate, metric + ) + log_likelihood <- get_mallows_loglik( + alpha_samples_ii, t(rho_samples_ii), n_items, + new_observed_rankings, metric + ) + } + out_loglik[tt] <- log_likelihood + } + + # ======================================================== # + # Test # + # ======================================================== # + tolerance <- 0.1 + expect_gt(max(out_loglik), mean(out_loglik) * (1 + tolerance)) + expect_lt(min(out_loglik), mean(out_loglik) * (1 - tolerance)) +}) diff --git a/tests/testthat/test-smc_mallows_new_item_rank.R b/tests/testthat/test-smc_mallows_new_item_rank.R new file mode 100644 index 00000000..c880b6e3 --- /dev/null +++ b/tests/testthat/test-smc_mallows_new_item_rank.R @@ -0,0 +1,109 @@ +context('SMC new user and item rank combined') + +# a simpler example to test ==================================================== +set.seed(101) +Time <- dim(sample_dataset)[3] + +# General ====================================================================== +n_items <- dim(sample_dataset)[2] # Number of items +rho_0 <- seq(from = 1, to = n_items, by = 1) # 'true' consensus ranking +alpha_0 <- 2 # fixed/ 'true' scale parameter +leap_size <- floor(n_items / 5) +metric <- "footrule" + +# Generate estimate of Z_n(alpha) ============================================== +alpha_vector <- seq(from = 0, to = 20, by = 0.1) +iter <- 1e2 +degree <- 10 + +# Estimate the logarithm of the partition function of the Mallows rank model +# using the estimate partition function +logz_estimate <- estimate_partition_function( + method = "importance_sampling", + alpha_vector = alpha_vector, + n_items = n_items, metric = metric, + nmc = iter, degree = degree +) + +mcmc_kernel_app <- 5 +N <- 20 +alpha_prop_sd <- 0.5 +lambda <- 0.15 +alpha_max <- 1e6 + +test_that("Produces the wrong metric and aug_method error", { + expect_error( + smc_mallows_new_item_rank_alpha_fixed( + alpha = alpha_0, n_items = n_items, R_obs = sample_dataset, + metric = "cayley", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "pseudolikelihood" + ) + ) + expect_error( + smc_mallows_new_item_rank( + n_items = n_items, R_obs = sample_dataset, + metric = "cayley", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "pseudolikelihood" + ) + ) +}) + +test_that("Runs with unif kernel", { + smc_unif_alpha_fixed_unif <- suppressMessages( + smc_mallows_new_item_rank_alpha_fixed( + alpha = alpha_0, n_items = n_items, R_obs = sample_dataset, + metric = "footrule", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "random" + ) + ) + expect_is(smc_unif_alpha_fixed_unif, "list") + expect_length(smc_unif_alpha_fixed_unif, 1) + expect_equal(dim(smc_unif_alpha_fixed_unif$rho_samples), c(N, 6, 31)) + smc_unif <- suppressMessages( + smc_mallows_new_item_rank( + n_items = n_items, R_obs = sample_dataset, + metric = "footrule", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "random" + ) + ) + expect_is(smc_unif, "list") + expect_length(smc_unif, 2) + expect_equal(dim(smc_unif$rho_samples), c(N, 6, 31)) + expect_equal(dim(smc_unif$alpha_samples), c(N, 31)) +}) + +test_that("Runs with pseudo kernel", { + smc_unif_alpha_fixed_unif <- suppressMessages( + smc_mallows_new_item_rank_alpha_fixed( + alpha = alpha_0, n_items = n_items, R_obs = sample_dataset, + metric = "footrule", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "pseudolikelihood" + ) + ) + expect_is(smc_unif_alpha_fixed_unif, "list") + expect_length(smc_unif_alpha_fixed_unif, 1) + expect_equal(dim(smc_unif_alpha_fixed_unif$rho_samples), c(N, 6, 31)) + smc_unif <- suppressMessages( + smc_mallows_new_item_rank( + n_items = n_items, R_obs = sample_dataset, + metric = "footrule", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "pseudolikelihood" + ) + ) + expect_is(smc_unif, "list") + expect_length(smc_unif, 2) + expect_equal(dim(smc_unif$rho_samples), c(N, 6, 31)) + expect_equal(dim(smc_unif$alpha_samples), c(N, 31)) +}) diff --git a/tests/testthat/test-smc_mallows_partial_rankings.R b/tests/testthat/test-smc_mallows_partial_rankings.R new file mode 100644 index 00000000..18dec112 --- /dev/null +++ b/tests/testthat/test-smc_mallows_partial_rankings.R @@ -0,0 +1,267 @@ +context("SMC new users partial rankings") + +# Generate Dataset ======================================= + +# General ------------------------------------------------ +n_items <- dim(sushi_rankings)[2] # Number of items +rho_0 <- seq(from = 1, to = n_items, by = 1) # 'true' consensus ranking +alpha_0 <- 2 # fixed/ 'true' scale parameter +leap_size <- floor(n_items / 5) +metric <- "footrule" + +# Generate estimate of Z_n(alpha) ------------------------ +alpha_vector <- seq(from = 0, to = 20, by = 1) +iter <- 1e2 +degree <- 10 + +# Estimate the logarithm of the partition function of the Mallows rank model +# using the estimate partition function +set.seed(994) +logz_estimate <- estimate_partition_function( + method = "importance_sampling", + alpha_vector = alpha_vector, + n_items = n_items, + metric = metric, + nmc = iter, + degree = degree +) + +# Make this information partially observed over time ----- +samples <- sushi_rankings[1: 100, ] +samples[samples > 5] <- NA + +# Bayesmallows MCMC Results ============================== +nmc <- 2000 +bm_mcmc <- compute_mallows( + samples, + nmc = nmc, + leap_size = leap_size, + metric = metric, + alpha_prop_sd = 0.15 +) +bm_mcmc$burnin <- 1000 + +# choice items to see in trace plot +items <- sample(1:n_items, 5, replace = FALSE) +items <- sort(items) + +test_that("BayesMallows MCMC Results are OK", { + rho_cp <- compute_consensus( + model_fit = bm_mcmc, + type = "CP", + burnin = bm_mcmc$burnin + ) + rho_map <- compute_consensus( + model_fit = bm_mcmc, + type = "MAP", + burnin = bm_mcmc$burnin + ) + post_rho <- compute_posterior_intervals(bm_mcmc, parameter = "rho") + post_alpha <- compute_posterior_intervals(bm_mcmc, parameter = "alpha") + expect_equal(dim(post_rho) , c(10, 7)) + expect_equal(dim(rho_cp) , c(10, 3)) + expect_equal(dim(rho_map) , c(10, 3)) + expect_equal(dim(post_alpha), c(1 , 6)) +}) + +# SMC Analysis (alpha unknown) =========================== + +mcmc_times <- 5 +num_new_obs <- 5 +Time <- dim(samples)[1] / num_new_obs +alpha_prop_sd <- 0.5 +lambda <- 0.15 +alpha_max <- 1e0 + +test_that("Produces the wrong metric and aug_method error", { + N <- 5 + expect_error( + smc_mallows_new_users_partial_alpha_fixed( + alpha = alpha_0, + R_obs = samples, + n_items = n_items, + metric = "cayley", + leap_size = leap_size, + N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = mcmc_times, + num_new_obs = num_new_obs, + aug_method = "pseudolikelihood" + ) + ) + expect_error( + smc_mallows_new_users_partial( + R_obs = samples, + n_items = n_items, + metric = "cayley", + leap_size = leap_size, + N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = mcmc_times, + num_new_obs = num_new_obs, + alpha_prop_sd = alpha_prop_sd, + lambda = lambda, + alpha_max = alpha_max, + aug_method = "pseudolikelihood" + ) + ) +}) +test_that("Runs with unif kernel", { + N <- 5 + smc_unif_alpha_fixed_unif <- smc_mallows_new_users_partial_alpha_fixed( + alpha = alpha_0, + R_obs = samples, + n_items = n_items, + metric = "footrule", + leap_size = leap_size, + N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = mcmc_times, + num_new_obs = num_new_obs, + aug_method = "random" + ) + expect_is(smc_unif_alpha_fixed_unif, "list") + expect_equal(length(smc_unif_alpha_fixed_unif), 1) + expect_equal(dim(smc_unif_alpha_fixed_unif$rho_samples), c(N, 10, 21)) + smc_unif <- smc_mallows_new_users_partial( + R_obs = samples, + n_items = n_items, + metric = "footrule", + leap_size = leap_size, + N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = mcmc_times, + num_new_obs = num_new_obs, + alpha_prop_sd = alpha_prop_sd, + lambda = lambda, + alpha_max = alpha_max, + aug_method = "random" + ) + expect_is(smc_unif, "list") + expect_equal(length(smc_unif), 2) + expect_equal(dim(smc_unif$rho_samples), c(N, 10, 21)) + expect_equal(dim(smc_unif$alpha_samples), c(N, 21)) + + expect_s3_class( + plot_alpha_posterior(smc_unif$alpha_samples[, Time+ 1], nmc = N, burnin = 2), + "ggplot") + + expect_s3_class( + plot_rho_posterior(smc_unif$rho_samples[, ,Time+ 1], nmc = N, burnin = 2, C = 1), + "ggplot") +}) + +test_that("Runs with pseudo kernel", { + N <- 5 + smc_unif_alpha_fixed_pseudo <- smc_mallows_new_users_partial_alpha_fixed( + alpha = alpha_0, + R_obs = samples, + n_items = n_items, + metric = "footrule", + leap_size = leap_size, + N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = mcmc_times, + num_new_obs = num_new_obs, + aug_method = "pseudolikelihood" + ) + expect_is(smc_unif_alpha_fixed_pseudo, "list") + expect_equal(length(smc_unif_alpha_fixed_pseudo), 1) + expect_equal(dim(smc_unif_alpha_fixed_pseudo$rho_samples), c(N, 10, 21)) + smc_pseudo <- smc_mallows_new_users_partial( + R_obs = samples, + n_items = n_items, + metric = "footrule", + leap_size = leap_size, + N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = mcmc_times, + num_new_obs = num_new_obs, + alpha_prop_sd = alpha_prop_sd, + lambda = lambda, + alpha_max = alpha_max, + aug_method = "pseudolikelihood" + ) + expect_is(smc_pseudo, "list") + expect_equal(length(smc_pseudo), 2) + expect_equal(dim(smc_pseudo$rho_samples), c(N, 10, 21)) + expect_equal(dim(smc_pseudo$alpha_samples), c(N, 21)) +}) + +# Specific example with random aug method ---------------- + +test_that("Specific example results are OK", { + N <- 10 + aug_method <- "random" + set.seed(5482) # necessary for reproducibility of the random aug_method + + test <- smc_mallows_new_users_partial( + R_obs = samples, + n_items = n_items, + metric = metric, + leap_size = leap_size, + N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = mcmc_times, + num_new_obs = num_new_obs, + alpha_prop_sd = alpha_prop_sd, + lambda = lambda, + alpha_max = alpha_max, + aug_method = aug_method + ) + rho_cp <- compute_rho_consensus( + output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0, C = 1, + type = "CP" + ) + set.seed(545) + rho_map <- compute_rho_consensus( + output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0, C = 1, + type = "MAP" + ) + post_rho <- compute_posterior_intervals_rho( + output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0 + ) + post_alpha <- compute_posterior_intervals_alpha( + output = test$alpha_samples[, Time + 1], nmc = N, burnin = 0 + ) + expect_equal(dim(rho_cp), c(10, 3)) + expect_equal(dim(rho_map), c(30, 3)) + expect_equal(dim(post_rho), c(10, 7)) + expect_equal(dim(post_alpha), c(1, 6)) + + test_fixed <- smc_mallows_new_users_partial_alpha_fixed( + R_obs = samples, + n_items = n_items, + metric = metric, + leap_size = leap_size, + N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = mcmc_times, + num_new_obs = num_new_obs, + aug_method = aug_method, + alpha = alpha_0 + ) + rho_cp_fixed <- compute_rho_consensus( + output = test_fixed$rho_samples[, , Time + 1], nmc = N, burnin = 0, C = 1, + type = "CP" + ) + set.seed(584) + rho_map_fixed <- compute_rho_consensus( + output = test_fixed$rho_samples[, , Time + 1], nmc = N, burnin = 0, C = 1, + type = "MAP" + ) + post_rho_fixed <- compute_posterior_intervals_rho( + output = test_fixed$rho_samples[, , Time + 1], nmc = N, burnin = 0 + ) + expect_equal(dim(rho_cp_fixed), c(10, 3)) + expect_equal(dim(rho_map_fixed), c(10, 3)) + expect_equal(dim(post_rho_fixed), c(10, 7)) +}) diff --git a/tests/testthat/test-smc_pseudolikelihood.R b/tests/testthat/test-smc_pseudolikelihood.R new file mode 100644 index 00000000..3790f744 --- /dev/null +++ b/tests/testthat/test-smc_pseudolikelihood.R @@ -0,0 +1,144 @@ +context("SMC pseudolikelihood functions") + +################################################################################ +# test for get_sample_probabilities +################################################################################ +set.seed(101) +rho <- c(1, 2, 3, 4, 5, 6) +alpha <- 2 +metric <- "footrule" +n_items <- 6 + +item_ordering <- c(3, 6, 4, 5) +partial_ranking <- c(1, 2, NA, NA, NA, NA) +remaining_set <- c(3, 4, 5, 6) +test_1 <- get_sample_probabilities( + rho_item_rank = rho[3], alpha, remaining_set, metric, n_items +) +test_2 <- get_sample_probabilities( + rho_item_rank = rho[4], alpha, remaining_set, metric, n_items +) + +test_that("get_sample_probabilities outputs as expected", { + expect_equivalent(test_1, c(0.38493, 0.27581, 0.19763, 0.14161), tol = 1e-4) + expect_equivalent(test_2, c(0.24318, 0.33938, 0.24318, 0.17424), tol = 1e-4) +}) + +################################################################################ +# test for calculate_forwards_probability and calculate_bacwards_probability +################################################################################ +set.seed(104) +rho <- c(1, 2, 3, 4, 5, 6) +alpha <- 2 +metric <- "footrule" +n_items <- 6 + +item_ordering <- c(3, 6, 4, 5) # order with which the ranks will be filled +partial_ranking <- c(1, 2, NA, NA, NA, NA) +remaining_set <- c(3, 4, 5, 6) + +test_1_forward <- calculate_forward_probability( + item_ordering = item_ordering, partial_ranking = partial_ranking, + remaining_set = remaining_set, rho = rho, alpha = alpha, + n_items = n_items, metric = metric +) # TODO #116: get this to output aug_ranking == c(1, 2, 3, 6, 5, 4). +# Tried all combinations of item_ordering. No dice. + +current_ranking <- c(1, 2, 6, 5, 4, 3) + +test_1_backward_a <- calculate_backward_probability( + item_ordering = item_ordering, partial_ranking = partial_ranking, + current_ranking = current_ranking, remaining_set = remaining_set, rho = rho, + alpha = alpha, n_items = n_items, metric = metric +) + +new_current_ranking <- test_1_forward$aug_ranking # c(1, 2, 3, 4, 6, 5) + +# new_current_ranking needs to be one of the following so that test_1_backward_b +# equals test_1_forward$forward_prob: +# - c(1, 2, 3, 6, 5, 4) +# - c(1, 2, 6, 4, 3, 5) +test_1_backward_b <- calculate_backward_probability( + item_ordering = item_ordering, partial_ranking = partial_ranking, + current_ranking = new_current_ranking, remaining_set = remaining_set, rho = rho, + alpha = alpha, n_items = n_items, metric = metric +) + +test_that("calculations of forward and backward probabilities", { + expect_equal(test_1_forward$aug_ranking, matrix(c(1, 2, 3, 6, 5, 4))) + expect_equal(test_1_forward$forward_prob, 0.03699547) + expect_equal(test_1_backward_a, 0.01360987) + expect_equal(test_1_backward_b, test_1_forward$forward_prob) +}) + +############################################ +# tests for M-H_aug_ranking_pseudo +########################################### + +set.seed(101) +rho <- c(1, 2, 3, 4, 5, 6) +alpha <- 2 +metric <- "footrule" +n_items <- 6 + +test_that("M-H aug ranking pseudo works", { + R_curr <- c(1, 2, 3, 4, 5, 6) + R_obs <- c(1, 2, 3, 4, 5, 6) + test_1 <- metropolis_hastings_aug_ranking_pseudo( + alpha, rho, n_items, R_obs, R_curr, metric + ) + expect_equal(test_1, matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(all(test_1 == R_curr), TRUE) + R_obs <- c(1, 2, 3, NA, NA, NA) + set.seed(6220) + test_2 <- metropolis_hastings_aug_ranking_pseudo( + alpha, rho, n_items, R_obs, R_curr, metric + ) + expect_equal(test_2, matrix(c(1, 2, 3, 5, 6, 4))) + expect_equal(all(test_2 == R_curr), FALSE) + R_curr <- c(1, 2, 6, 5, 4, 3) + R_obs <- c(1, 2, NA, NA, NA, NA) + set.seed(8286) + test_3 <- metropolis_hastings_aug_ranking_pseudo( + alpha, rho, n_items, partial_ranking = R_obs, current_ranking = R_curr, + metric + ) + expect_equal(test_3, matrix(c(1, 2, 4, 3, 5, 6))) + expect_equal(all(test_3 == R_curr), FALSE) +}) + +######################################################### +### tests relating to the correction_kernel function +######################################################### + +set.seed(101) +rho <- c(1, 2, 3, 4, 5, 6) +alpha <- 2 +metric <- "footrule" +n_items <- 6 + +test_that("correction_kernel works", { + # Three missing ------------------------------------------ # + R_curr <- c(1, 2, 3, 4, 5, 6) + R_obs <- c(1, 2, 3, NA, NA, NA) + test_1 <- correction_kernel_pseudo(R_curr, R_obs, rho, alpha, n_items, metric) + expect_equal(test_1$ranking, as.matrix(c(1, 2, 3, 5, 4, 6))) + expect_equivalent(test_1$correction_prob, 0.172, tol = 1e-2) + expect_equal(all(test_1$ranking == R_curr), FALSE) + + # Two missing -------------------------------------------- # + R_curr <- c(1, 2, 3, 4, 5, 6) + R_obs <- c(1, 2, 3, 5, NA, NA) + test_2 <- correction_kernel_pseudo(R_curr, R_obs, rho, alpha, n_items, metric) + expect_equal(test_2$ranking, as.matrix(c(1, 2, 3, 5, 4, 6))) + expect_equal(test_2$correction_prob, 0.5) + expect_equal(all(test_2$ranking == R_curr), FALSE) + + # No missing --------------------------------------------- # + R_curr <- c(1, 2, 3, 4, 5, 6) + R_obs <- c(1, 2, 3, 4, 5, 6) + test_3 <- correction_kernel_pseudo(R_curr, R_obs, rho, alpha, n_items, metric) + expect_equal(test_3$ranking, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(test_3$correction_prob, 1) + expect_equal(all(test_3$ranking == R_curr), TRUE) +}) diff --git a/tests/testthat/test-smc_uniform.R b/tests/testthat/test-smc_uniform.R new file mode 100644 index 00000000..e4075bc3 --- /dev/null +++ b/tests/testthat/test-smc_uniform.R @@ -0,0 +1,85 @@ +context("SMC uniform functions") + + set.seed(101) +require("BayesMallows") + +# tests for M-H_aug_ranking function =========================================== + +rho = c(1,2,3,4,5,6) +alpha = 2 +metric = "footrule" +n_items= 6 + +test_that("MH-aug ranking works", { + + # Three missing ranks ------------------------------------ # + R_curr <- c(1, 2, 3, 6, 5, 4) + R_obs <- c(1, 2, 3, NA, NA, NA) + set.seed(584) + test_1 <- metropolis_hastings_aug_ranking( + current_ranking = R_curr, + partial_ranking = R_obs, + alpha = alpha, + rho = rho, + n_items = n_items, + metric = metric + ) + expect_equal(test_1, as.matrix(c(1, 2, 3, 6, 5, 4))) + expect_equal(get_rank_distance(rho, test_1, metric = "ulam"), 2) + + # Three missing ranks, sequential current ranks ---------- # + R_curr <- rho + R_obs <- c(1, 2, 3, NA, NA, NA) + set.seed(866) + test_2 <- metropolis_hastings_aug_ranking( + current_ranking = R_curr, partial_ranking = R_obs, alpha = alpha, + rho = rho, n_items = n_items, metric = metric + ) + expect_equal(test_2, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(all(test_2 == rho), TRUE) + expect_equal(get_rank_distance(rho, test_2, metric = "ulam"), 0) + + # One missing rank --------------------------------------- # + R_curr <- c(1, 2, 3, 6, 5, 4) + R_obs <- c(1, 2, 3, 6, 5, NA) + set.seed(545) + test_3 <- metropolis_hastings_aug_ranking( + current_ranking = R_curr, partial_ranking = R_obs, alpha = alpha, + rho = rho, n_items = n_items, metric = metric + ) + expect_equal(test_3, as.matrix(c(1, 2, 3, 6, 5, 4))) + expect_equal(all(test_3 == R_curr), TRUE) +}) + +# tests relating to the correction_kernel function ============================= + +n_items <- 6 + +test_that("correction_kernel works", { + # Three missing ranks ------------------------------------ # + R_curr <- c(1, 2, 3, 4, 5, 6) + R_obs <- c(1, 2, 3, NA, NA, NA) + set.seed(879) + test_4 <- correction_kernel(R_obs, R_curr, n_items) + expect_equal(test_4$ranking, as.matrix(c(1, 2, 3, 6, 4, 5))) + expect_equal(test_4$correction_prob, 1 / 6) + expect_equal(all(test_4$ranking == R_curr), FALSE) + + # Two missing ranks -------------------------------------- # + R_curr <- c(1, 2, 3, 4, 5, 6) + R_obs <- c(1, 2, 3, 5, NA, NA) + set.seed(706) + test_5 <- correction_kernel(R_obs, R_curr, n_items) + expect_equal(test_5$ranking, as.matrix(c(1, 2, 3, 5, 4, 6))) + expect_equal(test_5$correction_prob, 0.5) + expect_equal(all(test_5$ranking == R_curr), FALSE) + + # No missing ranks --------------------------------------- # + R_curr <- c(1, 2, 3, 4, 5, 6) + R_obs <- c(1, 2, 3, 4, 5, 6) + set.seed(731) + test_6 <- correction_kernel(R_obs, R_curr, n_items) + expect_equal(test_6$ranking, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(test_6$correction_prob, 1) + expect_equal(all(test_6$ranking == R_curr), TRUE) +}) diff --git a/tests/testthat/test_transitive_closure.R b/tests/testthat/test-transitive_closure.R similarity index 100% rename from tests/testthat/test_transitive_closure.R rename to tests/testthat/test-transitive_closure.R diff --git a/tests/testthat/test_print.R b/tests/testthat/test_print.R deleted file mode 100644 index 0aff5bd8..00000000 --- a/tests/testthat/test_print.R +++ /dev/null @@ -1,27 +0,0 @@ -context("Testing print.BayesMallows and print.BayesMallowsMixtures") - -test_that("print.BayesMallows fails when it should", { - class(mtcars) <- "BayesMallows" - expect_error(print(mtcars)) - m <- compute_mallows(potato_visual, nmc = 5) - m$n_items <- NULL - expect_error(print(m), "BayesMallows object must have elements n_items and n_assessors.") - m$n_assessors <- NULL - expect_error(print(m), - "BayesMallows object must have elements n_items and n_assessors.") -}) - - -test_that("print.BayesMallowsMixtures fails when it should", { - class(mtcars) <- "BayesMallowsMixtures" - expect_error(print(mtcars), - regexp = "All elements of a BayesMallowsMixtures object must be of class BayesMallows.") - m <- compute_mallows_mixtures(potato_visual, n_clusters = 1:3, nmc = 5) - class(m[[1]]) <- "list" - expect_error(print(m), - regexp = "All elements of a BayesMallowsMixtures object must be of class BayesMallows.") - - m <- compute_mallows_mixtures(potato_visual, n_clusters = 1:3, nmc = 5) - expect_output(print(m), - "Collection of 3 Bayesian Mallows Mixture Models with the following number of mixture components:") -}) diff --git a/vignettes/SMC-Mallows.Rmd b/vignettes/SMC-Mallows.Rmd new file mode 100644 index 00000000..97fa8568 --- /dev/null +++ b/vignettes/SMC-Mallows.Rmd @@ -0,0 +1,369 @@ +--- +title: "SMC-Mallows Tutorial" +author: "Anja Stein" +date: "2021-10-25" +output: rmarkdown::html_vignette +bibliography: ../inst/REFERENCES.bib +link-citations: yes +vignette: > + %\VignetteIndexEntry{SMC-Mallows Tutorial} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +This vignette describes the extension to the `BayesMallows` R package called `SMC-Mallows`. This uses Sequential Monte Carlo (SMC) algorithms to provide updated approximations to the posterior distribution of a single Mallows model. We consider scenarios where we receive sequential information in the form of complete rankings, partial rankings and updated rankings from existing individuals who have previously provided a (partial) ranking. We use an alternative data augmentation method, called the pseudolikelihood approach, when we are using the footrule and Spearman distance functions instead of using an independent sampler. The extension currently uses functions, similiar to the `BayesMallows` R package, to visualise and analyse the posterior distributions. + + +## Overview of Extension + +### Set-up + +We begin by loading the following packages: + +```{r sushi_rankings_demo1, message=FALSE, warning=FALSE} +library(BayesMallows) +library(dplyr) +library(ggplot2) +``` + + +### Functions + +A list is provided with the most commonly used functions in this extension of the R package. + +| Function Name | Description | +|:-----------------------------|:--------------------------------------------| +| `smc_mallows_new_users_complete` | Runs the SMC algorithm for case where we observe full rankings as new observational data. | +| `smc_mallows_new_users_partial` | Runs the SMC algorithm for case where we observe partial rankings as new observational data. | +| `smc_mallows_new_item_rank` | Runs the SMC algorithm for case where we observe updated partial rankings as from existing users. | +| `plot_rho_posterior`| Plots posterior density of $\boldsymbol{\rho}$ for a selection of items. | +| `compute_posterior_intervals_rho` | Computes the Bayesian posterior intervals for $\boldsymbol{\rho}$.| +| `compute_consensus_rho`| Computes the CP estimate or MAP estimate of the latent ranks. | +| `plot_alpha_posterior` | Plots the posterior density of $\alpha$.| +| `compute_posterior_intervals_alpha` | Computes the Bayesian posterior intervals for $\alpha$.| + + + + +## Introduction + +We provide a summary on the Bayesian Mallows model and the proposed Sequential Monte Carlo framework which updates the parameter estimates of the posterior each time we receive new observations for a fixed computational cost. More information on the Bayesian Mallows model can found in @vitelli2018 and @liu2019, and a vignette on the `BayesMallows` R package can be found in @sorensen2020. A general discussion on SMC can be found in @del2006sequential and @doucet2009tutorial. + +### Notation + +We have set of $m$ distinct items, labelled $\mathcal{A} = \{A_1, \dots, A_m \}$, and we are asked to rank these items in order of personal preference with respect to some attribute. This information can be expressed as a ranking $\boldsymbol{R} = \{ R_1 , \dots , R_m\}$, which is a mapping $\boldsymbol{R}:\mathcal{A} \rightarrow \mathcal{P}_m$, where $\mathcal{P}_m$ is the space of $m$-dimensional permutations of $\{1, \dots, m\}$. Each $R_i \in \{1,\dots,m\}$ corresponds to the rank of an item $A_i$. We fundamentally assume that the preference information we receive from a group of individuals is transitive, i.e., each individual does not contradict themselves when specifying their preferences. In other words, for any three distinct items $\{A_i,A_j,A_k\}$ in a set, then if $A_i \prec A_j$ and $A_j \prec A_k$, then it must follow that $A_i \prec A_k$. Sometimes, we are unable to provide full rankings, so instead we provide a ranking for a subset of the items in $\mathcal{A}$. These are referred to as partial rankings. Partial rankings can occur either randomly or an individal can specify their top-k ranked items. In this scenario, we will need perform data augmentation in order to estimate the parameters of the Mallows model. + +### The Bayesian Mallows Model + +The Mallows model [@mallows1957] is a probability distribution for ranking data. The probability of observing a ranking $\boldsymbol{R}$ is defined as +$$p(\boldsymbol{R}) = p(\boldsymbol{R}|\boldsymbol{\rho},\alpha) =\frac{1}{Z_m(\alpha)} \exp \Big\{ -\frac{\alpha}{m} { d(\boldsymbol{R}, \boldsymbol{\rho})} \Big\},$$ + +where: $\boldsymbol{\rho} \in \mathcal{P}_m$ is the consensus ranking; $\alpha > 0$ is the scale parameter which represents the variability in rankings within the group of individuals around the consensus ranking; and $Z_m(\alpha)$ is the normalisation constant. The distance function, $d(\cdot,\cdot) : \mathcal{P}_m \times \mathcal{P}_m \rightarrow [0,\infty)$, measures the ''closeness'' of a ranking to the consensus ranking. The Mallows literature discusses the use of a right-invariant distance function, which means that the relabelling of items between two rankings is unaffected [@diaconis1988]. The distance metrics that the `BayesMallows` R package currently uses are: Footrule, Spearman, Cayley, Kendall and Hamming. This also means that the normalisation constant is independent of the consensus ranking. + +@vitelli2018 extended the Mallows model to incorporate a Bayesian framework for inference. A Uniform prior is elicited for the consensus ranking $\pi(\boldsymbol{\rho}) = (m!)^{-1} 1_{\mathcal{P}_m} (\boldsymbol{\rho})$ in the space of $\mathcal{P}_m$, and an Exponential prior for $\alpha$, with density $\pi(\alpha|\lambda) = \lambda \exp \{ -\lambda \alpha \} 1_{[0,\infty)}(\alpha)$. Given $M$ observed complete rankings and the prior distributions $\pi(\boldsymbol{\rho})$ and $\pi(\alpha)$, assuming prior independence of these variables, we have the following posterior density, known as the Bayesian Mallows model, + +$$ \pi(\boldsymbol\rho, \alpha | \boldsymbol{R}_1, \dots, \boldsymbol{R}_M) \propto \frac{\pi(\boldsymbol\rho)\pi(\alpha) }{[Z(\alpha)]^M} \exp \Bigg\{ - \frac{\alpha}{m} \sum_{j=1}^{M} d(\boldsymbol{R}_j, \boldsymbol\rho ) \Bigg\}.$$ + +Any posterior estimates of interest, such as the marginal posterior for $\boldsymbol{\rho}$, are obtained through the use of Metropolis-Hastings based Markov Chain Monte Carlo (MCMC) algorithm. Full details of the algorithm can be found in @vitelli2018. In each iteration of the algorithm, a new consensus ranking $\boldsymbol{\rho}'$ is proposed to update $\boldsymbol{\rho}$ according to a distribution which is centered around the current rank $\boldsymbol{\rho}$. The proposal step for $\boldsymbol{\rho}$ is done using the leap-and-shift proposal algorithm of @vitelli2018 and a new value $\alpha'$ is sampled from the log-Normal distribution to update the current value of $\alpha$. + +Inference on the Bayesian Mallows model can sample the posterior distribution of the unknown consensus ranking and scale parameter using a variety of observed data including: full rankings, incomplete rankings (e.g. top-$k$ rankings and ranks missing at random), and implicit data such as pairwise comparisons. For example, in the case of partial rankings, we can create augmented full ranking $\tilde{R}_1, \dots, \tilde{R}_M$ by using an independent sampler for each assessor containing the set of rankings not already chosen. The MCMC algorithm alternates between sampling a new value of $\boldsymbol{\rho}$ and $\alpha$ given the current $\tilde{R}_1, \dots, \tilde{R}_M$ and sampling $\tilde{R}_1, \dots, \tilde{R}_M$ given the current values of $\boldsymbol{\rho}$ and $\alpha$. The existing methods are discussed in @vitelli2018 and are provided in the `BayesMallows` R package [@sorensen2020]. + + +### Sequential Monte Carlo + +Sequential Monte Carlo (SMC) methods are a class of sampling algorithms which are used to estimate a sequence of target distributions given a stream of observations over discrete time. Each target distribution is approximated by a collection of random samples, termed particles, at each time step and evolve according to importance sampling and resampling steps. The literature on SMC methods is vast and diverse, but we are interested in using SMC as an alternative to MCMC methods [@chopin2002sequential]. A nice summary of the different variants of SMC is given in @del2006sequential. + +In SMC, the aim is to approximate a sequence of target distributions $\pi_t(\boldsymbol{\theta})$ with parameters $\boldsymbol{\theta}$ that we wish to estimate given a set of observed data $D_t$ which has accumulated up to time $t$. So we can write a target distribution $\pi_t$ as a posterior distribution +$$\pi_t(\boldsymbol{\theta}) = \pi_t(\boldsymbol{\theta} | D_t) \propto \pi_0(\boldsymbol{\theta})p_t(D_t| \boldsymbol{\theta}).$$ + +The SMC algorithm begins by generating $N$ particles using the prior distributions for each parameter and assigning each particle an equal weight. In each time step $t=1,\dots,T$, we assume that an additional $p$ independent observations $y_{1:p}$ become available from the target distribution. We reweight the particles in $\pi(\boldsymbol{\theta}| D_{t-1})$ from time $t-1$ to $t$ such that they are weighted with respect to $\pi(\boldsymbol{\theta}| D_{t})$, +$$ w^{(i)}_t = \frac{\pi(\boldsymbol{\theta}^{(i)}_{t-1} | D_{t})}{\pi(\boldsymbol{\theta}^{(i)}_{t-1} | D_{t-1})} \propto \frac{p(D_t | \boldsymbol{\theta}^{(i)}_{t-1})}{p(D_{t-1} | \boldsymbol{\theta}^{(i)}_{t-1})} = p( y_{1:p}| D_{t-1}, \boldsymbol{\theta}_{t-1}^{(i)} ), \ i = 1,\dots,N.$$ +Next, we normalise the particle weights and resample the particles with replacement which replicates the heavier weighted particles and discard those with negligible weights. This results in a set of equally weighted particles $\{ \boldsymbol{\theta}_t^{(i)}, w_t^{(i)} = \frac{1}{N} \}_{i=1}^N$ that represent a sample of the posterior distribution. A summary of the possible methods for resampling is given in @doucet2009tutorial. + +In the final stage, we move the particles using an MCMC kernel within SMC after resampling to give back the diversity of particle values [@berzuini2001resample]. This particular methodology in SMC is often referred to as the Resample-Move framework of @berzuini2001resample and @berzuini2003particle. We can apply MCMC kernel many times as we would like since the particles are still moving within the stationary distribution $\pi_t$. + + +## SMC-Mallows User Guide + +The `SMC-Mallows` extension contains functions which contain algorithms to perform the Resample-Move SMC framework of @berzuini2001resample using a single Mallows model. Each algorithm begins by drawing $N$ particles using the priors for $\boldsymbol{\rho}$ and $\alpha$ or by using specified initial values. Each particle is also assigned equal weight so at the start of the SMC algorithm we have $\{\boldsymbol{\theta}^{(i)}_0 = (\boldsymbol{\rho}_0^{(i)}, \alpha_0^{(i)}), w^{(i)} \}_{i=1}^{N}$ as the set of particles. Next, we observe some ranking data, $D_t$, and we calculate the updated weights of the particles with respect to the new observations and their contribution to the current estimated posterior distribution before reweighting and multinomial resampling. Finally, we perturb the particles using the Metropolis-Hastings based MCMC kernel and we use the proposal distributions described in @vitelli2018 for sampling values of $\boldsymbol{\rho}$ and $\alpha$. + + +### Complete Rankings + +For this case, we assume that we observe a collection of complete rankings from new assessors over a sequence of discrete time steps, $t=1,\dots, T$, such that up to a time $t$, we will have observed $|M_t|$ complete rankings. The particles are reweighted such that they are representative of the underlying distribution of the $|M_t|$ complete rankings. The new weights for each particle are calculated as + \begin{align*} + {w}^{(i)}_t(\boldsymbol{\theta}^{(i)}_{t-1}, \boldsymbol{\theta}^{(i)}_{t}) + &= \frac{ (Z_m(\alpha^{(i)}_{t-1}))^{-|M_t|} \exp \Big\{ - \frac{\alpha^{(i)}_{t-1}}{m} \sum_{j=1}^{|M_t|} d(\mathbf{R}^{(i)}_j, \boldsymbol{\rho}^{(i)}_{t-1} ) \Big\} }{ (Z_m(\alpha^{(i)}_{t-1}))^{-|M_{t-1}|} \exp \Big\{ - \frac{\alpha^{(i)}_{t-1}}{m} \sum_{j=1}^{|M_t|} d(\mathbf{R}^{(i)}_j, \boldsymbol{\rho}^{(i)}_{t-1} ) \Big\} } \\ + &= (Z_m(\alpha^{(i)}_{t-1}))^{-(|M_t|-|M_{t-1}|)}\exp \Bigg\{ - \frac{\alpha^{(i)}_{t-1}}{m} \sum_{j= |M_{t-1}|+1}^{|M_t|} d(\mathbf{R}^{(i)}_j, \boldsymbol{\rho}^{(i)}_{t-1} ) \Bigg\} , + \end{align*} + where $\alpha^{(i)}_{t-1}$ and $\boldsymbol{\rho}^{(i)}_{t-1}, \ i=1,\dots,N$ are the current estimated parameter values of the Mallows posterior before we reweight. + + +#### Demonstration + +We are interested in updating the parameter estimates of the Bayesian Mallows model based on the existing ranking data and new observations. We demonstrate the `SMC-Mallows` extension using the `sushi_rankings` dataset [@kamishima2003nantonac], which contains 5000 rankings for 10 sushi dishes. + +```{r sushi_rankings_demo} +head(sushi_rankings) +``` + +The function `smc_mallows_new_users_complete` shows the simplest version of the SMC algorithm in action. Many of the input variables, that the function requires, are also used in some of the existing functions in the `BayesMallows` R package. However, we will discuss what the new variables refer to in the algorithm. The variable `R_obs` in this instance is a 2D dataset with the number of columns as `n_items` and the number of rows, $M$, represents the number of individuals in the dataset. We begin the algorithm with no observations and in each artificial time step we introduce a batch number of observations, this is controlled by the variable `num_new_obs` to control the size of the bath, and `Time` to specify the number of updates we will perform. There is a limit on the value for `Time`, and this depends on the number of individuals in the dataset and the value of `num_new_obs`. In the move stage we can apply an MCMC kernel as many times as we would like using the variable `mcmc_kernel_app` to specify the value. + +```{r set_seed, message=FALSE, warning=FALSE, include=FALSE} +set.seed(994) +``` + +```{r smc_complete_set_up} +n_items <- dim(sushi_rankings)[2] +metric <- "footrule" +alpha_vector <- seq(from = 0, to = 15, by = 0.1) +iter <- 1e4 +degree <- 10 +# Estimate the logarithm of the partition function of the Mallows rank model +logz_estimate <- estimate_partition_function( + method = "importance_sampling", + alpha_vector = alpha_vector, + n_items = n_items, metric = metric, + nmc = iter, degree = degree +) +data <- sushi_rankings[1:100, ] +leap_size <- floor(n_items / 5) +N <- 1000 +Time <- 20 +smc_test <- smc_mallows_new_users_complete( + R_obs = data, n_items = n_items, + metric = metric, leap_size = leap_size, + N = N, Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = 5, + num_new_obs = 5, + alpha_prop_sd = 0.5, + lambda = 0.15, + alpha_max = 1e6 +) +``` + +The example `smc_test` returns a list variable of the particles values for $\boldsymbol{\rho}$ and $\alpha$ at each time step, so we can observe the posterior as it evolves. Specifically the list contains a three dimensional matrix of size `N` by `n_items` by `Time+1`, named `rho_samples`, and an `N` by `Time+1` matrix called `alpha_samples`. These matrices can be studied using some post-processing functions for visualising and analysing the posterior. It is also possible to make comparative results using the MCMC algorithm and the SMC algorithm. Unlike MCMC we cannot observe the trace plots and we do not need to specify a non-zero value for the burn-in. The indexing in R means that we have to view the `Time+1` slice of the output in order to view the posterior once all 100 rankings have been observed. + +Here, we can observe the posterior probabilities of $\boldsymbol{\rho}$ for a selection of items can be observed by calling the function `plot_rho_posterior`. + +```{r smc_complete_analysis_heatplot, message=FALSE, warning=FALSE} +test_sample_rho <- smc_test$rho_samples[, , Time + 1] +plot_rho_posterior( + output = test_sample_rho, + nmc = N, burnin = 0, C = 1, + colnames = colnames(sushi_rankings) +) +``` + +The posterior distributions of $\boldsymbol{\rho}$ and $\alpha$ can be studied with some of the the analysis tools provided by the extension. The posterior intervals for the consensus ranking of each sushi item are obtained by calling `compute_posterior_intervals_rho`. + +```{r posterior_intervals_rho, message=FALSE, warning=FALSE} +test_sample_rho <- smc_test$rho_samples[, , Time + 1] +compute_posterior_intervals_rho( + output = test_sample_rho, + nmc = N, burnin = 0, + verbose = FALSE +) +``` + +We can also rank the sushi items according to their cumulative probability (CP) consensus and their maximum posterior rankings (MAP). These are calculated by calling the function `compute_consensus_rho`. We demonstrate with the CP consensus: + +```{r consensus_ranking_estimates, message=FALSE, warning=FALSE} +compute_rho_consensus( + output = test_sample_rho, nmc = N, + burnin = 0, C = 1, type = "CP", + verbose = FALSE +) +``` + +Similarly, we can observe the posterior density and the posterior intervals for the scale parameter using the functions `plot_alpha_posterior` and `compute_posterior_intervals_alpha`. +```{r smc_complete_alpha_analysis, message=FALSE, warning=FALSE} +test_sample_alpha <- smc_test$alpha_samples[, Time + 1] +plot_alpha_posterior( + output = test_sample_alpha, nmc = N, + burnin = 0 +) +compute_posterior_intervals_alpha( + output = test_sample_alpha, + nmc = N, burnin = 0, verbose = FALSE +) +``` + +### Partial Rankings + +#### Pseudolikelihood Sampler +In @vitelli2018, the augmentation scheme for partially observed rankings is done by using an independent sampler conditioned in the observed component of each ranking. The drawback of this approach is that it does not take into account of the any existing information, such as the current estimates of the consensus ranking and the scale parameter. We have an option to use an alternative augmentation kernel where, for each item, we use a univariate Mallows distribution to select each rank to an item based on the item's rank in the estimated consensus ranking and the scale parameter. This approach is similar to the importance sampling approximation of the normalisation constant of the Bayesian Mallows model, full details can be found in @vitelli2018. This particular augmentation method only applies if we are using the footrule and Spearman distance metric. + + +#### New Assessors with Partial Rankings +We augment the missing item ranks in each $\mathbf{R}$ to create a complete auxiliary ranking $\tilde{\mathbf{R}}$ in order to perform the remaining steps of the SMC algorithm. We reweight the particles such that are representative of the underlying distribution of the $|M_t|$ augmented rankings. The particle weights are recalculated as + \begin{align*} + {w}^{(i)}_t(\boldsymbol{\theta}^{(i)}_{t-1}, \boldsymbol{\theta}^{(i)}_{t}) + &= (Z_m(\alpha^{(i)}_{t-1}))^{-(|M_t|-|M_{t-1}|)}\exp \Bigg\{ - \frac{\alpha^{(i)}_{t-1}}{m} \sum_{j= |M_{t-1}|+1}^{|M_t|} d(\tilde{\mathbf{R}}^{(i)}_j, \boldsymbol{\rho}^{(i)}_{t-1} ) \Bigg\} \\ + & \times \prod_{j = |M_{t-1}|+1}^{|M_t|} q(\tilde{\mathbf{R}}^{(i)}_j | \mathbf{R}_j, \boldsymbol{\rho}_{t-1}^{(i)}, \alpha_{t-1}^{(i)} ), + \end{align*} + where $\alpha^{(i)}_{t-1}$ and $\boldsymbol{\rho}^{(i)}_{t-1}, \ i=1,\dots,N$ are the current estimated parameter values of the Mallows posterior before we reweight. The distribution $q()$ represents the probability of creating an augmented ranking $\tilde{\mathbf{R}}$ given the observed ranking $\mathbf{R}$ and the current estimated parameters of the posterior. + + + +#### Demonstration +For this demonstration we shall assume that we can only observe the top-5 ranked items from each user in the first 100 rows of the `sushi_rankings` dataset. + +```{r smc_partial_set_up} +data_partial <- sushi_rankings[1:100, ] +data_partial[data_partial > 5] <- NA +head(data_partial) +``` + +We can call the function `smc_mallows_new_user_partial` to run the SMC algorithm for partial ranking data. The variable `aug_method` allows you to choose which data augmentation method to use on the partial ranking data. The option `"pseudolikelihood"` is only compatible with when selecting the distance metric as either `"footrule"` or `"spearman"`. + + +```{r smc_partial_error, eval=FALSE, error=TRUE} +aug_method <- "pseudolikelihood" +metric <- "cayley" +# example of selecting the incorrect combination of metric and aug_method +smc_partial_test <- smc_mallows_new_users_partial( + R_obs = data_partial, + n_items = n_items, + metric = metric, + leap_size = leap_size, N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = 5, + num_new_obs = 5, + alpha_prop_sd = 0.5, + lambda = 0.15, + alpha_max = 1e6, + aug_method = aug_method +) +#>Error in smc_mallows_new_users_partial(R_obs = data_partial, n_items = n_items, +#>: Combined choice of metric and aug_method is incompatible +``` + +This time we call the function again but this time we provide a valid combination of the augmentation method and the distance metric. +```{r set_seed2, message=FALSE, warning=FALSE, include=FALSE} +set.seed(994) +``` + + +```{r smc_partial_test} +# aug_method = "random" +aug_method <- "pseudolikelihood" +metric <- "footrule" +smc_partial_test <- smc_mallows_new_users_partial( + R_obs = data_partial, + n_items = n_items, + metric = metric, + leap_size = leap_size, N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = 5, + num_new_obs = 5, + alpha_prop_sd = 0.5, + lambda = 0.15, + alpha_max = 1e6, + aug_method = aug_method +) +``` + +The variable `smc_test_partial` contains a list with three dimensional matrix of size `N` by `n_items` by `Time+1`, named `rho_samples`, and a `N` by `Time+1` matrix called `alpha_samples`. The analysis performed in the previous demonstration can be applied to this scenario. Here, we can observe the posterior porbabilities for a selection of items in $\boldsymbol{\rho}$ and posterior density for $\alpha$ as part of this demonstration, but we can use other post processing functions to analyse the output. + +```{r smc_partial_analysis, message=FALSE, warning=FALSE} +partial_test_sample_rho <- smc_partial_test$rho_samples[, , Time + 1] +partial_test_sample_alpha <- smc_partial_test$alpha_samples[, Time + 1] +plot_rho_posterior( + output = partial_test_sample_rho, nmc = N, + burnin = 0, C = 1, colnames = colnames(sushi_rankings) +) +plot_alpha_posterior( + output = partial_test_sample_alpha, nmc = N, + burnin = 0 +) +``` + + +### Updated partial rankings + +We can view this scenario as a observing an updated partial ranking from an known individual, but a previously latent ranking for an unranked items becomes known. For example, an individual may provided a partial ranking for 6 items $(1,2,3,\texttt{NA},\texttt{NA},\texttt{NA})$ and in the SMC algorithm we might have obtained an augmented ranking $(1,2,3,5,6,4)$. However, later on the same individual may have provided more information about their ranking, e.g., $(1,2,3,4,\texttt{NA},\texttt{NA})$, and this ranking is no longer consistent with the augmented ranking. This causes a problem where we have an augmented complete ranking through SMC, conditioned on the original observed partial ranking, that happens to be no longer consistent with the new observed partial ranking. This means that our algorithm has an additional step before the reweighting stage. In order to reweight the particles correctly as a representative sample of the current posterior, we view the new information arriving into the system as an existing individual (and their augmented ranking) leaving the system completely and then re-entering as a new partial ranking with its extra information. For each individual leaving and re-entering the system, we have to make two weight adjustments to account for these actions. + + +#### Demonstration + +To illustrate how to perform SMC for updated partial rankings, we will modify a much smaller dataset called `potato_visual`. This dataset, which is described in @liu2019 and is provided in the `BayesMallows` R package, represents the rankings given by 12 assessors on 20 potatoes based on how heavy each potato appeared to be given their visual appearance. We create several partial subsets of the complete dataset by removing the lowest ranked item from each assessor from each previous subset, so that we achieve several partial datasets which view the top 10, top 11,... top 19 highest ranked items as well as the complete set of rankings. + +```{r smc_item_rank_set_up, message=FALSE, warning=FALSE} +example_dataset <- potato_visual +n_users <- 12 +n_items <- 20 +test_dataset <- array(0, c(n_users, n_items, (n_items / 2 + 1))) +test_dataset[, , (n_items / 2 + 1)] <- potato_visual +tt <- 0 +for (ii in (n_items - 1):(n_items / 2)) { + tt <- tt + 1 + + # set n_users line with one more NA + example_dataset[example_dataset > ii] <- NA + + # set as new time stamp + test_dataset[, , ((n_items / 2 + 1) - tt)] <- example_dataset +} +``` + +This means that we now have a 3D array containing 10 partial datasets and the one complete dataset. The third dimension of the array represents artifical time. We can view the updated partial rankings scenario as viewing several 2D slices of the observed dataset sequentially. So you can see, for example, that at the 5th time point, we observe the top-14 items from `potato_visual`. + +```{r test_dataset_example, message=FALSE, warning=FALSE} +test_dataset[, , 5] +``` + + +We can now run an experiment with the altered `potato_visual` dataset by calling the function `smc_mallows_new_item_rank`. +```{r set_seed3, message=FALSE, warning=FALSE, include=FALSE} +set.seed(995) +``` + + +```{r new_item_rank_example, message=FALSE, warning=FALSE} +# Recalculate the estimate of the partition function for 20 items +logz_estimate <- estimate_partition_function( + method = "importance_sampling", + alpha_vector = alpha_vector, + n_items = n_items, metric = metric, + nmc = iter, degree = degree +) +Time <- dim(test_dataset)[3] +N <- 1000 +aug_method <- "pseudolikelihood" +metric <- "footrule" +smc_test_updated_partial <- smc_mallows_new_item_rank( + n_items = n_items, + R_obs = test_dataset, + metric = metric, + leap_size = leap_size, N = N, + Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = 5, + alpha_prop_sd = 0.5, + lambda = 0.15, + alpha_max = 1e6, + aug_method = aug_method +) +``` + +Unlike the two previous demonstrations, the final slice of the output occurs at `Time` instead of `Time+1`. This is because we initialised the algorithm with the first slice of the `test_dataset` rather than initialising with no observed data. We observe the posterior probabilities for items in $\boldsymbol{\rho}$ and posterior density for $\alpha$ by using the same post-processing functions as before. + +```{r smc_updated_partial_analysis, message=FALSE, warning=FALSE} +updated_partial_test_sample_rho <- smc_test_updated_partial$rho_samples[, , Time] +updated_partial_test_sample_alpha <- smc_test_updated_partial$alpha_samples[, Time] +plot_rho_posterior(output = updated_partial_test_sample_rho, nmc = N, burnin = 0, C = 1) +plot_alpha_posterior(output = updated_partial_test_sample_alpha, nmc = N, burnin = 0) +``` + + + + +## References From 295b33df8cdc894447bd8a3d4a966aac9664adc9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Mon, 22 Nov 2021 15:26:26 +0100 Subject: [PATCH 20/41] corrected a bunch of typos and updated readme --- R/RcppExports.R | 32 +++++++++--------- R/data.R | 4 +-- R/generate_initial_ranking.R | 2 +- README.Rmd | 6 ++-- README.md | 14 +++++--- man/calculate_backward_probability.Rd | 4 +-- man/calculate_forward_probability.Rd | 2 +- man/correction_kernel_pseudo.Rd | 4 +-- man/generate_initial_ranking.Rd | 2 +- man/get_sample_probabilities.Rd | 2 +- man/metropolis_hastings_alpha.Rd | 2 +- man/metropolis_hastings_aug_ranking.Rd | 4 +-- man/metropolis_hastings_aug_ranking_pseudo.Rd | 4 +-- man/sample_dataset.Rd | 4 +-- man/smc_mallows_new_item_rank.Rd | 4 +-- man/smc_mallows_new_users_complete.Rd | 2 +- man/smc_mallows_new_users_partial.Rd | 2 +- ...c_mallows_new_users_partial_alpha_fixed.Rd | 2 +- src/smc_calculate_backward_probability.cpp | 4 +-- src/smc_calculate_forward_probability.cpp | 2 +- src/smc_correction_kernel_pseudo.cpp | 4 +-- src/smc_get_sample_probabilities.cpp | 2 +- src/smc_mallows_new_item_rank.cpp | 4 +-- src/smc_mallows_new_users_complete.cpp | 2 +- src/smc_mallows_new_users_partial.cpp | 2 +- ..._mallows_new_users_partial_alpha_fixed.cpp | 2 +- src/smc_metropolis_hastings_alpha.cpp | 4 +-- src/smc_metropolis_hastings_aug_ranking.cpp | 4 +-- ...metropolis_hastings_aug_ranking_pseudo.cpp | 4 +-- vignettes/BayesMallows.pdf | Bin 1545953 -> 0 bytes vignettes/SMC-Mallows.Rmd | 4 +-- 31 files changed, 71 insertions(+), 63 deletions(-) delete mode 100644 vignettes/BayesMallows.pdf diff --git a/R/RcppExports.R b/R/RcppExports.R index 8f5f0f2f..45d7a3e2 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -196,10 +196,10 @@ run_mcmc <- function(rankings, obs_freq, nmc, constraints, cardinalities, logz_e #' #' @param item_ordering A vector of integer values to represent the specified queue of which unranked item to assign a rank for the proposed augmented ranking #' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs -#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function #' @param remaining_set A vector of integer values to represent the elements (ranks) missing from original observed ranking #' @param rho Numeric vector specifying the consensus ranking -#' @param alpha Numeric value og the scale parameter +#' @param alpha Numeric value of the scale parameter #' @param n_items Integer is the number of items in a ranking #' @param metric A character string specifying the distance metric to use in the #' Bayesian Mallows Model. Available options are \code{"footrule"}, @@ -226,7 +226,7 @@ calculate_backward_probability <- function(item_ordering, partial_ranking, curre #' @param remaining_set A vector of integer values to represent the elements #' (ranks) missing from original observed ranking #' @param rho Numeric vector specifying the consensus ranking -#' @param alpha Numeric value og the scale parameter +#' @param alpha Numeric value of the scale parameter #' @param n_items Integer is the number of items in a ranking #' @param metric A character string specifying the distance metric to use in #' the Bayesian Mallows Model. Available options are \code{"footrule"}, @@ -264,9 +264,9 @@ correction_kernel <- function(observed_ranking, current_ranking, n_items) { #' If it is not, the we create a new augmentation using the pseudolikelihood approach and calculate the augmentation probability. #' #' @param observed_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs -#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function #' @param rho Numeric vector specifying the consensus ranking -#' @param alpha Numeric value og the scale parameter +#' @param alpha Numeric value of the scale parameter #' @param n_items Integer is the number of items in a ranking #' @param metric A character string specifying the distance metric to use in the #' Bayesian Mallows Model. Available options are \code{"footrule"}, @@ -328,7 +328,7 @@ get_mallows_loglik <- function(alpha, rho, n_items, rankings, metric) { #' given its rank in the consensus ranking #' #' @param rho_item_rank An integer value rank of an item in the current consensus ranking -#' @param alpha Numeric value og the scale parameter +#' @param alpha Numeric value of the scale parameter #' @param remaining_set_ranks A sequence of integer values of the set of possible ranks that we can assign the item #' @param metric A character string specifying the distance metric to use in the #' Bayesian Mallows Model. Available options are \code{"footrule"}, @@ -371,7 +371,7 @@ leap_and_shift_probs <- function(rho, leap_size, n_items) { #' @title SMC-Mallows new users rank #' @description Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user -#' at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudlikelihood augmentation. +#' at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudolikelihood augmentation. #' @param n_items Integer is the number of items in a ranking #' @param R_obs 3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps #' @param metric A character string specifying the distance metric to use in the @@ -384,7 +384,7 @@ leap_and_shift_probs <- function(rho, leap_size, n_items) { #' @param Time Integer specifying the number of time steps in the SMC algorithm #' @param logz_estimate Estimate of the partition function, computed with #' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. -#' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +#' @param mcmc_kernel_app Integer value for the number of applications we apply the MCMC move kernel #' @param alpha_prop_sd Numeric value of the standard deviation of the prior distribution for alpha #' @param lambda Strictly positive numeric value specifying the rate parameter #' of the truncated exponential prior distribution of alpha. @@ -453,7 +453,7 @@ smc_mallows_new_item_rank_alpha_fixed <- function(alpha, n_items, R_obs, metric, #' @param logz_estimate Estimate of the partition function, computed with #' \code{\link{estimate_partition_function}} in the BayesMallow R package #' {estimate_partition_function}. -#' @param mcmc_kernel_app Interger value for the number of applications we +#' @param mcmc_kernel_app Integer value for the number of applications we #' apply the MCMC move kernel #' @param num_new_obs Integer value for the number of new observations #' (complete rankings) for each time step @@ -495,7 +495,7 @@ smc_mallows_new_users_complete <- function(R_obs, n_items, metric, leap_size, N, #' @param Time Integer specifying the number of time steps in the SMC algorithm #' @param logz_estimate Estimate of the partition function, computed with #' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. -#' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +#' @param mcmc_kernel_app Integer value for the number of applications we apply the MCMC move kernel #' @param num_new_obs Integer value for the number of new observations (complete rankings) for each time step #' @param alpha_prop_sd Numeric value of the standard deviation of the prior distribution for alpha #' @param lambda Strictly positive numeric value specifying the rate parameter @@ -526,7 +526,7 @@ smc_mallows_new_users_partial <- function(R_obs, n_items, metric, leap_size, N, #' @param Time Integer specifying the number of time steps in the SMC algorithm #' @param logz_estimate Estimate of the partition function, computed with #' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. -#' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +#' @param mcmc_kernel_app Integer value for the number of applications we apply the MCMC move kernel #' @param num_new_obs Integer value for the number of new observations (complete rankings) for each time step #' @param aug_method A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random" #' @param alpha A numeric value of the scale parameter which is known and fixed @@ -539,7 +539,7 @@ smc_mallows_new_users_partial_alpha_fixed <- function(R_obs, n_items, metric, le #' @title Metropolis-Hastings Alpha #' @description Function to perform Metropolis-Hastings for new rho under #' the Mallows model with footrule distance metric! -#' @param alpha Numeric value og the scale parameter +#' @param alpha Numeric value of the scale parameter #' @param n_items Integer is the number of items in a ranking #' @param rankings the observed rankings, i.e, preference data #' @details \code{rankings} is a matrix of size @@ -576,11 +576,11 @@ metropolis_hastings_alpha <- function(alpha, n_items, rankings, metric, rho, log #' @title Metropolis-Hastings Augmented Ranking #' @description Function to perform Metropolis-Hastings for new augmented ranking #' -#' @param alpha Numeric value og the scale parameter +#' @param alpha Numeric value of the scale parameter #' @param rho Numeric vector specifying the consensus ranking #' @param n_items Integer is the number of items in a ranking #' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs -#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function #' @param metric A character string specifying the distance metric to use in the #' Bayesian Mallows Model. Available options are \code{"footrule"}, #' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and @@ -594,11 +594,11 @@ metropolis_hastings_aug_ranking <- function(alpha, rho, n_items, partial_ranking #' @title Metropolis-Hastings Augmented Ranking (pseudolikelihood) #' @description Function to perform Metropolis-Hastings for new augmented ranking using the pseudolikelihood augmentation approach #' -#' @param alpha Numeric value og the scale parameter +#' @param alpha Numeric value of the scale parameter #' @param rho Numeric vector specifying the consensus ranking #' @param n_items Integer is the number of items in a ranking #' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs -#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +#' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function #' @param metric A character string specifying the distance metric to use in the #' Bayesian Mallows Model. Available options are \code{"footrule"}, #' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and diff --git a/R/data.R b/R/data.R index 5b2f874e..7b5759a2 100644 --- a/R/data.R +++ b/R/data.R @@ -32,7 +32,7 @@ #' #' -#' A syntetic 3D matrix (\code{n_users}, \code{n_items}, \code{Time}) generated +#' A synthetic 3D matrix (\code{n_users}, \code{n_items}, \code{Time}) generated #' using the sample_mallows function. These are test datasets used to run #' the SMC-Mallows framework for the cases where we know all of the users #' in our system and their original ranking information are partial rankings. @@ -46,4 +46,4 @@ #' a complete dataset at \code{sample_dataset[, , Time]}. #' #' @references https://github.com/anjastein/SMC-Mallows/tree/main/data -"sample_dataset" \ No newline at end of file +"sample_dataset" diff --git a/R/generate_initial_ranking.R b/R/generate_initial_ranking.R index 225485b7..2709d684 100644 --- a/R/generate_initial_ranking.R +++ b/R/generate_initial_ranking.R @@ -15,7 +15,7 @@ #' \code{parallel::makeCluster}. Defaults to \code{NULL}. #' #' @param shuffle_unranked Logical specifying whether or not to randomly -#' permuted unranked items in the intial ranking. When +#' permuted unranked items in the initial ranking. When #' \code{shuffle_unranked=TRUE} and \code{random=FALSE}, all unranked items #' for each assessor are randomly permuted. Otherwise, the first ordering #' returned by \code{igraph::topo_sort()} is returned. diff --git a/README.Rmd b/README.Rmd index 6cd92532..563d9060 100644 --- a/README.Rmd +++ b/README.Rmd @@ -38,7 +38,7 @@ To install the current development version, use #install.packages("remotes") remotes::install_github("ocbe-uio/BayesMallows") -# Set build_vignettes to TRUE to also get the package vignette +# Set build_vignettes to TRUE to also get the package vignettes remotes::install_github("ocbe-uio/BayesMallows", build_vignettes = TRUE) ``` @@ -81,13 +81,15 @@ vignette("BayesMallows") The BayesMallows package currently implements the complete model described in @vitelli2018, which includes a large number of distance metrics, handling of missing ranks and pairwise comparisons, and clustering of users with similar preferences. The extension to non-transitive pairwise comparisons by @crispino2019 is also implemented. In addition, the partition function of the Mallows model can be estimated using the importance sampling algorithm of @vitelli2018 and the asymptotic approximation of @mukherjee2016. For a review of ranking models in general, see @liu2019. @crispino2019informative outlines how informative priors can be used within the model. +Updating of the posterior distribution based on new data, using sequential Monte Carlo methods, is implemented and described in a separate vignette which can be shown with the command `vignette("SMC-Mallows")`. + ### Applications Among the current applications, @liu2019b applied the Bayesian Mallows model for providing personalized recommendations based on clicking data, and @barrett2018 used the model of @crispino2019 to analyze listeners' understanding of music. ### Future Extensions -Plans for future extensions of the package include allowing for analysis of time-varying ranks as described in @asfaw2016, (frequentist) maximum likelihood estimation, and implementation of a variational Bayes algorithm for approximation the posterior distribution. +Plans for future extensions of the package include allowing for analysis of time-varying ranks as described in @asfaw2016, and implementation of a variational Bayes algorithm for approximation the posterior distribution. The sequential Monte Carlo algorithms will also be extended to cover a larger part of the model framework. ## Citation diff --git a/README.md b/README.md index 78958458..4071cd6b 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ To install the current development version, use #install.packages("remotes") remotes::install_github("ocbe-uio/BayesMallows") -# Set build_vignettes to TRUE to also get the package vignette +# Set build_vignettes to TRUE to also get the package vignettes remotes::install_github("ocbe-uio/BayesMallows", build_vignettes = TRUE) ``` @@ -91,6 +91,11 @@ review of ranking models in general, see Q. Liu et al. (2019b). Crispino and Antoniano-Villalobos (2019) outlines how informative priors can be used within the model. +Updating of the posterior distribution based on new data, using +sequential Monte Carlo methods, is implemented and described in a +separate vignette which can be shown with the command +`vignette("SMC-Mallows")`. + ### Applications Among the current applications, Q. Liu et al. (2019a) applied the @@ -101,9 +106,10 @@ Crispino et al. (2019) to analyze listeners’ understanding of music. ### Future Extensions Plans for future extensions of the package include allowing for analysis -of time-varying ranks as described in Asfaw et al. (2016), (frequentist) -maximum likelihood estimation, and implementation of a variational Bayes -algorithm for approximation the posterior distribution. +of time-varying ranks as described in Asfaw et al. (2016), and +implementation of a variational Bayes algorithm for approximation the +posterior distribution. The sequential Monte Carlo algorithms will also +be extended to cover a larger part of the model framework. ## Citation diff --git a/man/calculate_backward_probability.Rd b/man/calculate_backward_probability.Rd index 0e16195a..71ac908a 100644 --- a/man/calculate_backward_probability.Rd +++ b/man/calculate_backward_probability.Rd @@ -20,13 +20,13 @@ calculate_backward_probability( \item{partial_ranking}{An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs} -\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function} +\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function} \item{remaining_set}{A vector of integer values to represent the elements (ranks) missing from original observed ranking} \item{rho}{Numeric vector specifying the consensus ranking} -\item{alpha}{Numeric value og the scale parameter} +\item{alpha}{Numeric value of the scale parameter} \item{n_items}{Integer is the number of items in a ranking} diff --git a/man/calculate_forward_probability.Rd b/man/calculate_forward_probability.Rd index 41ca69d4..21ba0f72 100644 --- a/man/calculate_forward_probability.Rd +++ b/man/calculate_forward_probability.Rd @@ -27,7 +27,7 @@ observed incomplete ranking which contains NAs} \item{rho}{Numeric vector specifying the consensus ranking} -\item{alpha}{Numeric value og the scale parameter} +\item{alpha}{Numeric value of the scale parameter} \item{n_items}{Integer is the number of items in a ranking} diff --git a/man/correction_kernel_pseudo.Rd b/man/correction_kernel_pseudo.Rd index 743441c3..77ec7bdb 100644 --- a/man/correction_kernel_pseudo.Rd +++ b/man/correction_kernel_pseudo.Rd @@ -14,13 +14,13 @@ correction_kernel_pseudo( ) } \arguments{ -\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function} +\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function} \item{observed_ranking}{An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs} \item{rho}{Numeric vector specifying the consensus ranking} -\item{alpha}{Numeric value og the scale parameter} +\item{alpha}{Numeric value of the scale parameter} \item{n_items}{Integer is the number of items in a ranking} diff --git a/man/generate_initial_ranking.Rd b/man/generate_initial_ranking.Rd index fab94653..a8b20558 100644 --- a/man/generate_initial_ranking.Rd +++ b/man/generate_initial_ranking.Rd @@ -26,7 +26,7 @@ c("bottom_item", "top_item")])}.} \code{parallel::makeCluster}. Defaults to \code{NULL}.} \item{shuffle_unranked}{Logical specifying whether or not to randomly -permuted unranked items in the intial ranking. When +permuted unranked items in the initial ranking. When \code{shuffle_unranked=TRUE} and \code{random=FALSE}, all unranked items for each assessor are randomly permuted. Otherwise, the first ordering returned by \code{igraph::topo_sort()} is returned.} diff --git a/man/get_sample_probabilities.Rd b/man/get_sample_probabilities.Rd index 103465b7..2e80ab4c 100644 --- a/man/get_sample_probabilities.Rd +++ b/man/get_sample_probabilities.Rd @@ -15,7 +15,7 @@ get_sample_probabilities( \arguments{ \item{rho_item_rank}{An integer value rank of an item in the current consensus ranking} -\item{alpha}{Numeric value og the scale parameter} +\item{alpha}{Numeric value of the scale parameter} \item{remaining_set_ranks}{A sequence of integer values of the set of possible ranks that we can assign the item} diff --git a/man/metropolis_hastings_alpha.Rd b/man/metropolis_hastings_alpha.Rd index 8040e920..ea9d14a1 100644 --- a/man/metropolis_hastings_alpha.Rd +++ b/man/metropolis_hastings_alpha.Rd @@ -17,7 +17,7 @@ metropolis_hastings_alpha( ) } \arguments{ -\item{alpha}{Numeric value og the scale parameter} +\item{alpha}{Numeric value of the scale parameter} \item{n_items}{Integer is the number of items in a ranking} diff --git a/man/metropolis_hastings_aug_ranking.Rd b/man/metropolis_hastings_aug_ranking.Rd index dc96c0e9..67e6f39d 100644 --- a/man/metropolis_hastings_aug_ranking.Rd +++ b/man/metropolis_hastings_aug_ranking.Rd @@ -14,7 +14,7 @@ metropolis_hastings_aug_ranking( ) } \arguments{ -\item{alpha}{Numeric value og the scale parameter} +\item{alpha}{Numeric value of the scale parameter} \item{rho}{Numeric vector specifying the consensus ranking} @@ -22,7 +22,7 @@ metropolis_hastings_aug_ranking( \item{partial_ranking}{An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs} -\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function} +\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function} \item{metric}{A character string specifying the distance metric to use in the Bayesian Mallows Model. Available options are \code{"footrule"}, diff --git a/man/metropolis_hastings_aug_ranking_pseudo.Rd b/man/metropolis_hastings_aug_ranking_pseudo.Rd index bd0e3d3e..3fd452c3 100644 --- a/man/metropolis_hastings_aug_ranking_pseudo.Rd +++ b/man/metropolis_hastings_aug_ranking_pseudo.Rd @@ -14,7 +14,7 @@ metropolis_hastings_aug_ranking_pseudo( ) } \arguments{ -\item{alpha}{Numeric value og the scale parameter} +\item{alpha}{Numeric value of the scale parameter} \item{rho}{Numeric vector specifying the consensus ranking} @@ -22,7 +22,7 @@ metropolis_hastings_aug_ranking_pseudo( \item{partial_ranking}{An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs} -\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function} +\item{current_ranking}{An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function} \item{metric}{A character string specifying the distance metric to use in the Bayesian Mallows Model. Available options are \code{"footrule"}, diff --git a/man/sample_dataset.Rd b/man/sample_dataset.Rd index 978705f9..e623a332 100644 --- a/man/sample_dataset.Rd +++ b/man/sample_dataset.Rd @@ -3,7 +3,7 @@ \docType{data} \name{sample_dataset} \alias{sample_dataset} -\title{A syntetic 3D matrix (\code{n_users}, \code{n_items}, \code{Time}) generated +\title{A synthetic 3D matrix (\code{n_users}, \code{n_items}, \code{Time}) generated using the sample_mallows function. These are test datasets used to run the SMC-Mallows framework for the cases where we know all of the users in our system and their original ranking information are partial rankings. @@ -22,7 +22,7 @@ An object of class \code{array} of dimension 10 x 6 x 31. sample_dataset } \description{ -A syntetic 3D matrix (\code{n_users}, \code{n_items}, \code{Time}) generated +A synthetic 3D matrix (\code{n_users}, \code{n_items}, \code{Time}) generated using the sample_mallows function. These are test datasets used to run the SMC-Mallows framework for the cases where we know all of the users in our system and their original ranking information are partial rankings. diff --git a/man/smc_mallows_new_item_rank.Rd b/man/smc_mallows_new_item_rank.Rd index 10319151..ca3d6d38 100644 --- a/man/smc_mallows_new_item_rank.Rd +++ b/man/smc_mallows_new_item_rank.Rd @@ -40,7 +40,7 @@ proposal distribution} \item{logz_estimate}{Estimate of the partition function, computed with \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}.} -\item{mcmc_kernel_app}{Interger value for the number of applications we apply the MCMC move kernel} +\item{mcmc_kernel_app}{Integer value for the number of applications we apply the MCMC move kernel} \item{alpha_prop_sd}{Numeric value of the standard deviation of the prior distribution for alpha} @@ -60,5 +60,5 @@ a 3d matrix containing the samples of rho and alpha from the SMC algorithm } \description{ Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user -at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudlikelihood augmentation. +at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudolikelihood augmentation. } diff --git a/man/smc_mallows_new_users_complete.Rd b/man/smc_mallows_new_users_complete.Rd index 1be80a2b..95bc025a 100644 --- a/man/smc_mallows_new_users_complete.Rd +++ b/man/smc_mallows_new_users_complete.Rd @@ -38,7 +38,7 @@ leap-and-shift proposal distribution} \item{Time}{Integer specifying the number of time steps in the SMC algorithm} -\item{mcmc_kernel_app}{Interger value for the number of applications we +\item{mcmc_kernel_app}{Integer value for the number of applications we apply the MCMC move kernel} \item{num_new_obs}{Integer value for the number of new observations diff --git a/man/smc_mallows_new_users_partial.Rd b/man/smc_mallows_new_users_partial.Rd index 87dbf3d6..cdceca59 100644 --- a/man/smc_mallows_new_users_partial.Rd +++ b/man/smc_mallows_new_users_partial.Rd @@ -41,7 +41,7 @@ proposal distribution} \item{logz_estimate}{Estimate of the partition function, computed with \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}.} -\item{mcmc_kernel_app}{Interger value for the number of applications we apply the MCMC move kernel} +\item{mcmc_kernel_app}{Integer value for the number of applications we apply the MCMC move kernel} \item{num_new_obs}{Integer value for the number of new observations (complete rankings) for each time step} diff --git a/man/smc_mallows_new_users_partial_alpha_fixed.Rd b/man/smc_mallows_new_users_partial_alpha_fixed.Rd index d41193aa..1b16392f 100644 --- a/man/smc_mallows_new_users_partial_alpha_fixed.Rd +++ b/man/smc_mallows_new_users_partial_alpha_fixed.Rd @@ -38,7 +38,7 @@ proposal distribution} \item{logz_estimate}{Estimate of the partition function, computed with \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}.} -\item{mcmc_kernel_app}{Interger value for the number of applications we apply the MCMC move kernel} +\item{mcmc_kernel_app}{Integer value for the number of applications we apply the MCMC move kernel} \item{num_new_obs}{Integer value for the number of new observations (complete rankings) for each time step} diff --git a/src/smc_calculate_backward_probability.cpp b/src/smc_calculate_backward_probability.cpp index bbad48e9..d8fd648d 100644 --- a/src/smc_calculate_backward_probability.cpp +++ b/src/smc_calculate_backward_probability.cpp @@ -9,10 +9,10 @@ //' //' @param item_ordering A vector of integer values to represent the specified queue of which unranked item to assign a rank for the proposed augmented ranking //' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs -//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function //' @param remaining_set A vector of integer values to represent the elements (ranks) missing from original observed ranking //' @param rho Numeric vector specifying the consensus ranking -//' @param alpha Numeric value og the scale parameter +//' @param alpha Numeric value of the scale parameter //' @param n_items Integer is the number of items in a ranking //' @param metric A character string specifying the distance metric to use in the //' Bayesian Mallows Model. Available options are \code{"footrule"}, diff --git a/src/smc_calculate_forward_probability.cpp b/src/smc_calculate_forward_probability.cpp index 64009eb1..b9c618be 100644 --- a/src/smc_calculate_forward_probability.cpp +++ b/src/smc_calculate_forward_probability.cpp @@ -17,7 +17,7 @@ //' @param remaining_set A vector of integer values to represent the elements //' (ranks) missing from original observed ranking //' @param rho Numeric vector specifying the consensus ranking -//' @param alpha Numeric value og the scale parameter +//' @param alpha Numeric value of the scale parameter //' @param n_items Integer is the number of items in a ranking //' @param metric A character string specifying the distance metric to use in //' the Bayesian Mallows Model. Available options are \code{"footrule"}, diff --git a/src/smc_correction_kernel_pseudo.cpp b/src/smc_correction_kernel_pseudo.cpp index acd4f638..74ec74a7 100644 --- a/src/smc_correction_kernel_pseudo.cpp +++ b/src/smc_correction_kernel_pseudo.cpp @@ -7,9 +7,9 @@ //' If it is not, the we create a new augmentation using the pseudolikelihood approach and calculate the augmentation probability. //' //' @param observed_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs -//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function //' @param rho Numeric vector specifying the consensus ranking -//' @param alpha Numeric value og the scale parameter +//' @param alpha Numeric value of the scale parameter //' @param n_items Integer is the number of items in a ranking //' @param metric A character string specifying the distance metric to use in the //' Bayesian Mallows Model. Available options are \code{"footrule"}, diff --git a/src/smc_get_sample_probabilities.cpp b/src/smc_get_sample_probabilities.cpp index fd3c7d32..0a942d3b 100644 --- a/src/smc_get_sample_probabilities.cpp +++ b/src/smc_get_sample_probabilities.cpp @@ -7,7 +7,7 @@ //' given its rank in the consensus ranking //' //' @param rho_item_rank An integer value rank of an item in the current consensus ranking -//' @param alpha Numeric value og the scale parameter +//' @param alpha Numeric value of the scale parameter //' @param remaining_set_ranks A sequence of integer values of the set of possible ranks that we can assign the item //' @param metric A character string specifying the distance metric to use in the //' Bayesian Mallows Model. Available options are \code{"footrule"}, diff --git a/src/smc_mallows_new_item_rank.cpp b/src/smc_mallows_new_item_rank.cpp index 341ccdd1..3e0795f8 100644 --- a/src/smc_mallows_new_item_rank.cpp +++ b/src/smc_mallows_new_item_rank.cpp @@ -6,7 +6,7 @@ // [[Rcpp::depends(RcppArmadillo)]] //' @title SMC-Mallows new users rank //' @description Function to perform resample-move SMC algorithm where we receive a new item ranks from an existing user -//' at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudlikelihood augmentation. +//' at each time step. Each correction and augmentation is done by filling in the missing item ranks using pseudolikelihood augmentation. //' @param n_items Integer is the number of items in a ranking //' @param R_obs 3D matrix of size n_assessors by n_items by Time containing a set of observed rankings of Time time steps //' @param metric A character string specifying the distance metric to use in the @@ -19,7 +19,7 @@ //' @param Time Integer specifying the number of time steps in the SMC algorithm //' @param logz_estimate Estimate of the partition function, computed with //' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. -//' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +//' @param mcmc_kernel_app Integer value for the number of applications we apply the MCMC move kernel //' @param alpha_prop_sd Numeric value of the standard deviation of the prior distribution for alpha //' @param lambda Strictly positive numeric value specifying the rate parameter //' of the truncated exponential prior distribution of alpha. diff --git a/src/smc_mallows_new_users_complete.cpp b/src/smc_mallows_new_users_complete.cpp index e52e284e..14fc5800 100644 --- a/src/smc_mallows_new_users_complete.cpp +++ b/src/smc_mallows_new_users_complete.cpp @@ -21,7 +21,7 @@ //' @param logz_estimate Estimate of the partition function, computed with //' \code{\link{estimate_partition_function}} in the BayesMallow R package //' {estimate_partition_function}. -//' @param mcmc_kernel_app Interger value for the number of applications we +//' @param mcmc_kernel_app Integer value for the number of applications we //' apply the MCMC move kernel //' @param num_new_obs Integer value for the number of new observations //' (complete rankings) for each time step diff --git a/src/smc_mallows_new_users_partial.cpp b/src/smc_mallows_new_users_partial.cpp index 70a36d94..e3850fb7 100644 --- a/src/smc_mallows_new_users_partial.cpp +++ b/src/smc_mallows_new_users_partial.cpp @@ -19,7 +19,7 @@ //' @param Time Integer specifying the number of time steps in the SMC algorithm //' @param logz_estimate Estimate of the partition function, computed with //' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. -//' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +//' @param mcmc_kernel_app Integer value for the number of applications we apply the MCMC move kernel //' @param num_new_obs Integer value for the number of new observations (complete rankings) for each time step //' @param alpha_prop_sd Numeric value of the standard deviation of the prior distribution for alpha //' @param lambda Strictly positive numeric value specifying the rate parameter diff --git a/src/smc_mallows_new_users_partial_alpha_fixed.cpp b/src/smc_mallows_new_users_partial_alpha_fixed.cpp index 54acc8f3..6883958a 100644 --- a/src/smc_mallows_new_users_partial_alpha_fixed.cpp +++ b/src/smc_mallows_new_users_partial_alpha_fixed.cpp @@ -19,7 +19,7 @@ //' @param Time Integer specifying the number of time steps in the SMC algorithm //' @param logz_estimate Estimate of the partition function, computed with //' \code{\link{estimate_partition_function}} in the BayesMallow R package {estimate_partition_function}. -//' @param mcmc_kernel_app Interger value for the number of applications we apply the MCMC move kernel +//' @param mcmc_kernel_app Integer value for the number of applications we apply the MCMC move kernel //' @param num_new_obs Integer value for the number of new observations (complete rankings) for each time step //' @param aug_method A character string specifying the approach for filling in the missing data, options are "pseudolikelihood" or "random" //' @param alpha A numeric value of the scale parameter which is known and fixed diff --git a/src/smc_metropolis_hastings_alpha.cpp b/src/smc_metropolis_hastings_alpha.cpp index 3fe15765..d60e6010 100644 --- a/src/smc_metropolis_hastings_alpha.cpp +++ b/src/smc_metropolis_hastings_alpha.cpp @@ -6,7 +6,7 @@ //' @title Metropolis-Hastings Alpha //' @description Function to perform Metropolis-Hastings for new rho under //' the Mallows model with footrule distance metric! -//' @param alpha Numeric value og the scale parameter +//' @param alpha Numeric value of the scale parameter //' @param n_items Integer is the number of items in a ranking //' @param rankings the observed rankings, i.e, preference data //' @details \code{rankings} is a matrix of size @@ -75,4 +75,4 @@ double metropolis_hastings_alpha( } else { return(alpha); } -} \ No newline at end of file +} diff --git a/src/smc_metropolis_hastings_aug_ranking.cpp b/src/smc_metropolis_hastings_aug_ranking.cpp index b0ada3d6..e85ef7c8 100644 --- a/src/smc_metropolis_hastings_aug_ranking.cpp +++ b/src/smc_metropolis_hastings_aug_ranking.cpp @@ -5,11 +5,11 @@ //' @title Metropolis-Hastings Augmented Ranking //' @description Function to perform Metropolis-Hastings for new augmented ranking //' -//' @param alpha Numeric value og the scale parameter +//' @param alpha Numeric value of the scale parameter //' @param rho Numeric vector specifying the consensus ranking //' @param n_items Integer is the number of items in a ranking //' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs -//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function //' @param metric A character string specifying the distance metric to use in the //' Bayesian Mallows Model. Available options are \code{"footrule"}, //' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and diff --git a/src/smc_metropolis_hastings_aug_ranking_pseudo.cpp b/src/smc_metropolis_hastings_aug_ranking_pseudo.cpp index 44a8ebf0..c09dcf3f 100644 --- a/src/smc_metropolis_hastings_aug_ranking_pseudo.cpp +++ b/src/smc_metropolis_hastings_aug_ranking_pseudo.cpp @@ -5,11 +5,11 @@ //' @title Metropolis-Hastings Augmented Ranking (pseudolikelihood) //' @description Function to perform Metropolis-Hastings for new augmented ranking using the pseudolikelihood augmentation approach //' -//' @param alpha Numeric value og the scale parameter +//' @param alpha Numeric value of the scale parameter //' @param rho Numeric vector specifying the consensus ranking //' @param n_items Integer is the number of items in a ranking //' @param partial_ranking An incomplete rank sequence vector of the original observed incomplete ranking which contains NAs -//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obatined from calculate_forward_probability function +//' @param current_ranking An complete rank sequence vector of the proposed augmented ranking obtained from calculate_forward_probability function //' @param metric A character string specifying the distance metric to use in the //' Bayesian Mallows Model. 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zm?1)XU~nKyyUm%QCIcL|d2yi8_0XjK&FxU>)iEv9lPr=9++(Tw??fm0P zFX0LUqox86m#|M1mBBH8Dl2j+o3aUJ{~o(k!oCtBuzyL#f zv_Bih-9$SnJz#REj9Cz7SRSAb!9IQppv7R4kV$YfnDRgJlmyHD6#R3bJK-uAls zmoeHIGIFQMlohq$ljd#=T~&JsxH{oDG2-ZGIFK9H#p)nZKwrNVkI|>8^W+3`{rEw> z!TzhEjzU`9=}3wJCbL(D-4Cq}KPkYW4qC}iQXcX#z&j6b-VdP+(iw~IA`IsOdNn1( zkG;?&Z_rlL$ye#FK~Pf{SAJ3u?wsX~Qi0KW=5A;;{$9b_s9JCh`%O8Yuuck-8`M*A z2S)Gb4+6+@k>EYQjZ!$@(lN-`Mff zx5(x4nJpcVxoxsEz?+p0GFFeDm_QR<7Nnyd5k@x&4p^UFf7NL^RcwIbHjop1aGZ#L zcJ9wM0VnccPteMnt|D8Dgd3(7SZWWZ9U*7*SrEbu{m6q8}EbLDINWTcq&m|&e8roB8aY6bup1weknn2PM7=qUD?rHip~#4 z2JfSo63)E6nvq%jTG~?d`fMf*y`U+gT<^$YVk=8H?pJmx0j~;qQ}R#u?6Bt}XQbWS ztLFPqVo?>JCwv)kzAf_;!Viyk*a(@64}>%jn$uDchZploAJbonfFWeH*O4ta)XBZ% zA*xszqD)@)7gV|}f<<1d+vWZG5yM2{Kc^f5Gm8Hu#%h|JnbPL11$KEYusr$9@3Z-- z64mnRyx@Z<^H#Vi-IVPX!`X2DjV12b0)bC)f2i3E4I)ziVC%LWWK!P{xH_SAdBv=J zd(ysuX6U4Zbnypn3p%cU5n%HVAx_z+a3L|Ng8t~ysY6x*w_&M4{gr!`!ce(H#!3S3 zkOxqdR{PKtz}_9bNSbg45!!>=ojhkhI}Ff+Mnw* zmHtMu5==mKn7NqriNSw*r1|vUlP^NH3^mM6Jyht7@xSR5@{e^b&z5a9_@^XZ6bPEl*3q7wAlQrN{qj dXdRvO9h}_0T}U_p8xY9+?JiMLiphz?{XbMkGFSir literal 0 HcmV?d00001 From 7edab0bc4a953aac206e785ce5e47f7ea3ecce17 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Fri, 3 Dec 2021 08:53:07 +0100 Subject: [PATCH 23/41] Convert MAP output to tibble (fixes #163) (#164) * Added test script for compute_rho_consensus (#163) * Minor test optimizations Restyling + shortening * Casting df as tibble (fixes #163) Co-authored-by: Anja Stein --- R/smc_post_processing_functions.R | 2 + .../testthat/test-smc_compute_rho_consensus.R | 71 +++++++++++++++++++ 2 files changed, 73 insertions(+) create mode 100644 tests/testthat/test-smc_compute_rho_consensus.R diff --git a/R/smc_post_processing_functions.R b/R/smc_post_processing_functions.R index 492da009..73d9c5c9 100644 --- a/R/smc_post_processing_functions.R +++ b/R/smc_post_processing_functions.R @@ -223,6 +223,8 @@ find_cpc_smc <- function(group_df){ df <- dplyr::select(df, -.data$cluster) } + df <- dplyr::as_tibble(df) # added to solve issue #163. Remove for # 162. + return(df) } diff --git a/tests/testthat/test-smc_compute_rho_consensus.R b/tests/testthat/test-smc_compute_rho_consensus.R new file mode 100644 index 00000000..97bd3358 --- /dev/null +++ b/tests/testthat/test-smc_compute_rho_consensus.R @@ -0,0 +1,71 @@ +context("Further tests on compute_rho_consensus") +set.seed(1234) + +#################################### +# generate example for testing +#################################### +n_items <- dim(sushi_rankings)[2] +metric <- "footrule" +alpha_vector <- seq(from = 0, to = 15, by = 0.1) +iter <- 1e2 +degree <- 10 + +# Estimate the logarithm of the partition function of the Mallows rank model +logz_estimate <- estimate_partition_function( + method = "importance_sampling", + alpha_vector = alpha_vector, + n_items = n_items, metric = metric, + nmc = iter, degree = degree +) + +data <- sushi_rankings[1:100, ] +leap_size <- floor(n_items / 5) +N <- 100 +Time <- 20 + +model_fit <- smc_mallows_new_users_complete( + R_obs = data, n_items = n_items, + metric = metric, leap_size = leap_size, + N = N, Time = Time, + logz_estimate = logz_estimate, + mcmc_kernel_app = 5, + num_new_obs = 5, + alpha_prop_sd = 0.5, + lambda = 0.15, + alpha_max = 1e6 +) + +################## +# Run tests +################## +test_sample_rho <- model_fit$rho_samples[, , Time + 1] + +test1 <- compute_rho_consensus( + output = test_sample_rho, nmc = N, + burnin = 0, C = 1, type = "CP", + verbose = FALSE +) + +test2 <- compute_rho_consensus( + output = test_sample_rho, nmc = N, + burnin = 0, C = 1, type = "MAP", + verbose = FALSE +) + +test_that("Output of compute_rho_consensus (CP) is OK", { + expect_is(test1, "tbl_df") + expect_length(test1, 3) + expect_named(test1, c("ranking", "item", "cumprob")) + expect_equal(dim(test1), c(n_items, 3)) + expect_equal(sum(table(test1$ranking)), n_items) + expect_equal(sum(table(test1$item)), n_items) +}) + +test_that("Output of compute_rho_consensus (MAP) is OK", { + expect_is(test2, "tbl_df") + expect_length(test2, 3) + expect_named(test2, c("probability", "item", "map_ranking")) + expect_equal(dim(test2), c(n_items, 3)) + expect_equal(sum(table(test2$map_ranking)), n_items) + expect_equal(sum(table(test2$item)), n_items) +}) From 15519d75be808e7a10dc6333bdca60a7af70c369 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Fri, 3 Dec 2021 12:48:36 +0100 Subject: [PATCH 24/41] Changed classes for compute_posterior_interval methods (#80) --- R/compute_posterior_intervals.R | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/R/compute_posterior_intervals.R b/R/compute_posterior_intervals.R index 3e31a13a..ac468847 100644 --- a/R/compute_posterior_intervals.R +++ b/R/compute_posterior_intervals.R @@ -63,13 +63,13 @@ compute_posterior_intervals.BayesMallows <- function( if(parameter == "alpha" || parameter == "cluster_probs"){ df <- dplyr::group_by(df, .data$cluster) - class(df) <- c("BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") + class(df) <- c("posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") df <- .compute_posterior_intervals(df, parameter, level, decimals) } else if(parameter == "rho"){ decimals <- 0 df <- dplyr::group_by(df, .data$cluster, .data$item) - class(df) <- c("BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") + class(df) <- c("posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") df <- .compute_posterior_intervals(df, parameter, level, decimals, discrete = TRUE) } @@ -105,12 +105,12 @@ compute_posterior_intervals.SMCMallows <- function( if (parameter == "alpha" || parameter == "cluster_probs") { df <- dplyr::group_by(df, .data$cluster) - class(df) <- c("SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame") + class(df) <- c("posterior_SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame") df <- .compute_posterior_intervals(df, parameter, level, decimals) } else if (parameter == "rho") { decimals <- 0 df <- dplyr::group_by(df, .data$cluster, .data$item) - class(df) <- c("SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame") + class(df) <- c("posterior_SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame") df <- .compute_posterior_intervals(df, parameter, level, decimals, discrete = TRUE) } @@ -123,7 +123,7 @@ compute_posterior_intervals.SMCMallows <- function( return(df) } -.compute_posterior_intervals.BayesMallows <- function( +.compute_posterior_intervals.posterior_BayesMallows <- function( df, parameter, level, decimals, discrete = FALSE, ... ){ dplyr::do(df, { @@ -186,7 +186,7 @@ compute_posterior_intervals.SMCMallows <- function( # same as compute_posterior_intervals, but removed the bayesmallows object and # some other columns -.compute_posterior_intervals.SMCMallows <- function( +.compute_posterior_intervals.posterior_SMCMallows <- function( df, parameter, level, decimals, discrete = FALSE, ... ) { dplyr::do(df, { From 8e31043ce34f8b49df25326c75f204b718d5c482 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Fri, 3 Dec 2021 12:12:05 +0100 Subject: [PATCH 25/41] Added tests to check BM and SMC classes (#80) --- .../test-compute_consensus_workflow.R | 120 ++++++++++++++++++ 1 file changed, 120 insertions(+) create mode 100644 tests/testthat/test-compute_consensus_workflow.R diff --git a/tests/testthat/test-compute_consensus_workflow.R b/tests/testthat/test-compute_consensus_workflow.R new file mode 100644 index 00000000..045a5556 --- /dev/null +++ b/tests/testthat/test-compute_consensus_workflow.R @@ -0,0 +1,120 @@ +# This script checks the correct usage of the generic functions created during +# the fix of issue 80. +set.seed(6998768) + +context("compute_posterior_interval() classes") + +# Typical BayesMallows workflow ================================================ + +fit_bm <- compute_mallows(potato_visual) +fit_bm$burnin <- 1000 +fit_bm_post_alpha <- compute_posterior_intervals(fit_bm, parameter = "alpha") +fit_bm_post_rho <- compute_posterior_intervals(fit_bm, parameter = "rho") + +# Typical SMC-Mallows workflow ================================================= + +n_items <- ncol(sushi_rankings) +metric <- "footrule" +alpha_vector <- seq(from = 0, to = 15, by = 0.1) +iter <- 1e3 +degree <- 10 +logz_estimate <- estimate_partition_function( + method = "importance_sampling", alpha_vector = alpha_vector, + n_items = n_items, metric = metric, nmc = iter, degree = degree +) +data <- sushi_rankings[1:100, ] +leap_size <- floor(n_items / 5) +nmc <- N <- 1000 +Time <- 20 +fit_smc <- smc_mallows_new_users_complete( + R_obs = data, n_items = n_items, metric = metric, leap_size = leap_size, + N = N, Time = Time, logz_estimate = logz_estimate, mcmc_kernel_app = 5, + num_new_obs = 5, alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 1e6 +) +fit_smc_alpha <- fit_smc$alpha_samples[, Time + 1] +fit_smc_post_alpha <- compute_posterior_intervals_alpha( + output = fit_smc_alpha, nmc = nmc, burnin = 0, verbose = FALSE +) +fit_smc_rho <- fit_smc$rho_samples[, , Time + 1] +fit_smc_post_rho <- compute_posterior_intervals_rho( + output = fit_smc_rho, nmc = nmc, burnin = 0, + verbose = FALSE +) + +# Emulating the internal workings of compute_posterior_intervals =============== + +# BayesMallows ------------------------------------------- # + +fit_bm_alpha <- fit_bm$alpha +fit_bm_alpha <- dplyr::group_by(fit_bm_alpha, .data$cluster) +class(fit_bm_alpha) <- c( + "posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame" +) +fit_bm_post_internal_alpha <- .compute_posterior_intervals( + fit_bm_alpha, "alpha", .95, 3L +) + +fit_bm_rho <- fit_bm$rho +fit_bm_rho <- dplyr::group_by(fit_bm_rho, .data$cluster) +class(fit_bm_rho) <- c( + "posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame" +) +fit_bm_post_internal_rho <- .compute_posterior_intervals( + fit_bm_rho, "rho", .95, 3L +) + +# SMC-Mallows -------------------------------------------- # + +fit_smc_alpha <- data.frame(iteration = seq_len(nmc), value = fit_smc_alpha) +fit_smc_alpha$n_clusters <- 1 +fit_smc_alpha$cluster <- "Cluster 1" +fit_smc_alpha <- dplyr::group_by(fit_smc_alpha, .data$cluster) +class(fit_smc_alpha) <- c( + "posterior_SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame" +) +fit_smc_post_internal_alpha <- .compute_posterior_intervals( + fit_smc_alpha, "alpha", .95, 3L +) + +fit_smc_rho <- smc_processing(fit_smc_rho) +fit_smc_rho$n_clusters <- 1 +fit_smc_rho$cluster <- "Cluster 1" +fit_smc_rho <- dplyr::group_by(fit_smc_rho, .data$cluster) +class(fit_smc_rho) <- c( + "posterior_SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame" +) +fit_smc_post_internal_rho <- .compute_posterior_intervals( + fit_smc_alpha, "rho", .95, 3L, discrete = TRUE +) + +# Testing classes ============================================================== + +test_that("Classes are correctly attributed", { + expect_s3_class(fit_bm, "BayesMallows") + expect_s3_class(fit_smc, "SMCMallows") + expect_s3_class(fit_bm_post_alpha, "data.frame") + expect_s3_class(fit_bm_post_rho, "data.frame") + expect_s3_class(fit_smc_post_alpha, "data.frame") + expect_s3_class(fit_smc_post_rho, "data.frame") + expect_error(.compute_posterior_intervals(fit_bm_post_alpha)) + expect_error(.compute_posterior_intervals(fit_bm_post_rho)) + expect_error(.compute_posterior_intervals(fit_smc_post_alpha)) + expect_error(.compute_posterior_intervals(fit_smc_post_rho)) + expect_s3_class(fit_bm_post_internal_alpha, "data.frame") + expect_s3_class(fit_bm_post_internal_rho, "data.frame") + expect_s3_class(fit_smc_post_internal_alpha, "data.frame") + expect_s3_class(fit_smc_post_internal_rho, "data.frame") +}) + +context("compute_consensus() classes") + +fit_bm_consensus_cp <- compute_consensus(fit_bm, type = "CP") +fit_bm_consensus_map <- compute_consensus(fit_bm, type = "MAP") +# TODO #80: add tests for SMC (compute_rho_consensus). Read +# https://github.com/ocbe-uio/BayesMallows/issues/80#issuecomment-984444753 for +# details + +test_that("Classes are correctly attributed", { + expect_s3_class(fit_bm_consensus_cp, "data.frame") + expect_s3_class(fit_bm_consensus_map, "data.frame") +}) From e2a7df65c4fc33fe0228f4118c129f9fa33bbe39 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Fri, 3 Dec 2021 17:11:30 +0100 Subject: [PATCH 26/41] Implement generics to close #80 (#161) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Replaced Rcpp vector with arma vector (#90) * Removed TODO For the moment, smc_mallows_new_users_complete() will still use Rcpp::sample() to calculate the arma::uvec index. Replacement of Rcpp::sample() with arma::rand*() is encouraged, but left to any future developer aware of how to include probability weights on arma::rand*(). For more details, see conversation in issue #90. * Rearranged inline comments Top comment was relocated and rewritten on https://github.com/ocbe-uio/BayesMallows/issues/90#issuecomment-866614296 for better visibility and organization. Mid-code comment added for future reference. * Syntax fix * Reduced example length (closes #96) * Increment version number * Reduced unit test parameters (resolves #100) * Dropped usage of deprecated function (closes #99) * Added original scripts (#106) * Fitting scripts to package format (#106) * Added original scripts * Removed source calls, moved unit tests * Syntax fixes * Updated docs, exported new functions * Added placeholders for unit tests * Commented out failing tests These tests trigger errors on get_rank_distance(), to be investigated later (see issue #98 comments section for more details). * Increment version number * Increment version number * Syntax fix aug_prob is a vector, so we need to select just one element to perform those operations. * Fixed function name Function was using a deprecated, alternative name. * Fixed calls to get_mallows_loglik (#98, #109) * Reduced number of iterations on test * Restyled file * Fixes tests (#109) * Implemented tests (#98) * Edited test titles * Removed TODO * Improved SMC tests' speed (fixes #102) * Moved get_sample_probabilities() to src folder * Translated get_sample_probabilities + docs (#98) * Moved calculate_forward_probability to src folder * Exporting empty C++ function * Partial translation of calculate_forward_probability * Added get_sample_probabilities to smc.h * Finished translation of calculate_forward_probability Also: - Updated build version + Rcpp exports - Increment version number * Updated unit test expectations * Removed unnecessary library call * Translated calculate_backward_probability to C++ * Increment version number * Syntax fixes + refactoring * Changed file Unix permissions to match other files Used chmod to change the file permissions from 755 to 644 so it matches the rest of the files on the folder. * Added files for SMC-Mallows new item rank * Updated SMC partial rankings functions and tests * Restyled files * Syntax fixes * Commented out code to be worked on (#110) * Increment version number * Syntax fix * Formatted unit tests (#110) * Reduced test time (#110) Further test optimization Check fixes * Improvements to validation * Added SMC dataset * Improved partial ranking tests (#110) * Improved API feedback * Adapted test units for new item rank (#110) * Optimized unit tests (#110) * Minor fixes for clean check * Translated metropolis_hastings_aug_ranking_pseudo (#98) - Added skeleton of C++ metropolis_hastings_aug_ranking_pseudo - Translated metropolis_hastings_aug_ranking_pseudo - Replaced R version with C++ version - Fixed behavior of metropolis_hastings_aug_ranking_pseudo - Improved test format and updated expectations * Increment version number and R version dependency * Refactoring for easier debugging * Fixed RNG behavior on unit test * Removed superseded function * Added translation of metropolis_hastings_aug_ranking * Fixed documentation The docs for metropolis_hastings_aug_ranking_CPP() had text from the pseudo function. * Replaced R version of mh_aug_ranking with C++ (#98) Also, adjusted unit test expectations accordingly * Increment version number * Adjusted tests and renamed arguments (#98) * Added original code for correction_kernel C++ (#111) * Syntax fixes * Increment version number * Replaced R with C++ version of correction_kernel (#111) * Refactoring * Code optimization * Renamed cpp file * Added original code for correction_kernel_pseudo C++ (#111) * Syntax fixes * Replaced R with C++ of correction_kernel_pseudo (#111) * Increment version number * Replaced calculation of remaining_set * Translated smc_mallows_new_users_partial (#107) - Added C++ skeleton of smc_mallows_new_users_partial - Translated function * Set more seeds in unit tests This avoids random errors in unit testing. * Increment version number * Fixed calculation on smc_mallows_new_users_complete * Replaced R version with C++ version (#107) Of smc_mallows_new_users_partial(). * cal_fwd_prob() now works with both R and C++ indices * Refactoring * Adjusting unit tests * Returning test units to their original expectation * Updated Roxygen version * cal_bwd_prob() now works with both R and C++ indices This is basically a copy-paste from commit ce5d52cd. I smell a function coming... * Refactoring * Adjusting test expectations to new code output The differing output is caused by `set.seed(101)` having a different effect on the new C++ functions, particularly the output of `test_1_forward$aug_ranking`, which trickles down into the calculation of `test_1_backward_b`, thus causing different probabilities. The code was back-checked with the original R code, in which case the output seems the same. However, this should be checked with Anja. * Moved common code into function As "promised" on e497763640992f788c4ff0c98f6f127b99d4d785. * Translated partial ranks alpha fixed to C++ (#107) - Added skeleton of C++ alpha_fixed function - Finished translation - Switched R and C++ versions - Updated unit test expectations - Removed the R version of the function * Added parameters to SMC new users complete Added alpha_prob_sd, lambda and alpha_max, which were new arguments to metropolis_hastings_alpha and were internally defined as constants. * Added knitr as vignette engine Now both R.rsp and knitr are used as vignette builders. The former is used to add the BM paper in PDF format, the latter is hosting the SMC Rmarkdown vignette (yet a mockup). * Added mockup of SMC vignette * Updated .gitignore Update generated by `devtools::build_vignettes()`. * Updated build version * Translated new_item_rank to C++ (#113) * Replaced R new_item_rank with C++ version (#113) * Increment version number * Opening CI to all branches r.yml is currently configured for only triggering GitHub Actions CI workflow for the `master` and the `main` (inexistent here) branches. Opening it up to any branch so commits to `smc-mallows` also get triggered. This is important so we can better foresee problems in the upcoming merge with `master`. Opening up to any branch name shouldn't be a problem on this repository, since `master` is the only long-lived branch at the moment. * Syntax fixes * Refactoring * More specific seed setting * Fixing tests for Ubuntu Tests for Mac and Windows are still failing due to RNG. To be addressed later. * Investigate tests (#127) * Added prints statements to `arma::shuffle` * Added unique sort to `setdiff` * Updated tests Co-authored-by: Øystein Sørensen * Updates to smc_post_processing_functions.R (#119) * Updates to smc_post_processing_functions.R Hi, Just done a bit of tidying with the post_processing_functions. Here is a summary of my proposed changes: 1. edited `smc_processing` to allow the option to print the `col_names` of the original dataset if appropriate 2. edited `compute_consensus` to print the output correctly in the `MAP` case and to print the `colnames` of a dataset if appropriate instead of listing items as 'item 1, item 2, ... etc'. 3. edited `plot_posterior_intervals_rho` to print the `colnames` of a dataset if appropriate instead of listing items as 'item 1, item 2, ... etc'. 4. adjusted the font sizes of the axis labels in `plot_heatplot_rho` 5. removed the variable `verbose` from `plot_posterior_alpha` so that it now prints when calling the functions and doesn't do duplicate plots. 6. commented out any unused post-processing functions I have added some general comments, which begin with `#AS:` to explain why I have made the following changes (even though Github will highlight them). Let me know if you disagree or think any changes are unnecessary. I am planning on renaming the variables in the functions `heatMat` and `heatplot_fixed` so that they are consistent with the other heatplot functions, but I shall do this once this pull request has been completed/resolved(?). Waldir's changes: * Changed test unit expectations * Documentation fixes * Removed commented-out code * Replaced dplyr call with base R Co-authored-by: Waldir Leoncio * Updated smc.rmd Copy-pasted vignette script to github. Need to upload the matching .bib file containing the references. * create vignette.bib This file contains the references to be printed in the smc.Rmd file * Remove unused columns The final columns of rho_samples and alpha_samples do not get populated with non-zero values, so they are redundant. The same change applies to smc_mallows_new_item_rank_alpha_fixed, so this will appear as another pull request. * Remove unused column Extra column in rho_samples is redundant * Update compute_posterior_intervals_rho Reordered column in output to print item names in correct numerical order "item 1, item 2, ..., item 10" instead of "item 1, item 10, item 2, ..., item 9" * Cosmetic changes to heatplot functions Renamed variables to keep variable names consistent. Added some comments to explain the steps in the functions. * Updated test expectation Test units should be reset to expect one fewer columns, given the most recent code changes proposed. * Updated test expectations The previous changes on this branch reduce the number of columns by 1, so this must be reflected in the relevant unit test expectations. * Syntax fixes + doc update * Syntax fixes * Increment version number * Syntax adjustments * Changed test expectations back to what they should be See comment https://github.com/ocbe-uio/BayesMallows/issues/116#issuecomment-938583180 for more details. * Trying to reproduce Anja's seed and output * Increment version number * Added C++ version of smc_mallows_new_item_rank_alpha_fixed (#113) * Replaced R with C++ of smc_mallows_new_item_rank_alpha_fixed (#113) * Added C++ function of SMC new item alpha fixed (#113) * Added C++ version of smc_mallows_new_item_rank_alpha_fixed (#113) * Replaced R with C++ of smc_mallows_new_item_rank_alpha_fixed (#113) * Added C++ function of SMC new item alpha fixed (#113) * Added unit tests for #134 Co-authored-by: Waldir Leoncio Co-authored-by: Anja Stein * Reduced test time * get_mallows_loglik now transposes as needed (#134) This eliminated the bug when passing rankings as a row vector and rho as a column vector (or vice-versa). Both still need to be matrices, though. * Increment version Increment version number to 1.0.3.9002 Increment version number to 1.0.3.9003 Increment version number to 1.0.3.9004 * Updated build version * Exported all functions from #117 - Exported `plot_rho_heatplot()` - Exported `plot_alpha_posterior()` - Exported `compute_posterior_intervals_alpha()` - Exported `compute_posterior_intervals_rho()` - Exported `compute_rho_consensus()` Also, some function names were renamed in comments and the SMC vignette text due to slight differences between their names there and the one on the code. The names on the code were preferred. Namely, the changes were: 1. `plot_heatplot_rho` into `plot_rho_heatplot` 2. `compute_consensus_rho` into `compute_rho_consensus` * Refactoring * Increment version number to 1.0.3.9006 * Added print argument to internal function * Updated test expectation, added comments * Added temporary print statements for debugging * Increment version number to 1.0.3.9007 * Added function plot_rho_posterior.R in smc_post_processing_functions.R (#137) * Added function plot_rho_posterior.R in smc_post_processing_functions.R * Synxtax fixes + whitespace removal * Exporting function with documentation Co-authored-by: Waldir Leoncio * Changing output of SMC function to new class Including change in test expectation. * Added generics, methods for compute_posterior_intervals * Changed classes of Mallows objects (#80) Generics and methods related to `compute_posterior_intervals()` had some class additions to allow for better dispatching between BayesMallows and SMC-Mallows methods. * Moved methods to same file (#80) Moved all methods of `compute_posterior_intervals()` and `.compute_posterior_intervals()` to the same file (easier to manage). * Syntax and documentation fixes/updates * Increment version number to 1.0.3.9006 * Updated TODOs * Update smc.Rmd Removed the heatplot function and replaced it with the plot rho posterior function. Some descriptions have changed to fit the description of the posterior function. * Remove heatplot functions Since we now have the `plot_rho_posterior` function to clearer visualisation, we can discontinue this function. * Deleted commented-out code * Updated docs * Removed unit test for dropped function * Increment version number to 1.0.3.9007 * Moved gtools from Suggests to Depends Also, standardized indentation. * Updated build version * added print statements to calculate_forward_probability.cpp Added lots of print statements to help with debugging * added print statements to calculate_backwards_probability.cpp added extra print statements for debugging * Fixed bug causing issue #116 Co-authored-by: Waldir Leoncio Co-authored-by: Anja Stein * Removed temporary code * Updated test expectations * Removed gtools dependency on SMC (#142) Removed dependency on gtools::mixedorder Works by extracting everything that isn't a digit ([0-9]) and ultimately using the index of the sorted elements to sort `rho_posterior_interval`. Works well for the standard "Item DD" format that SMC uses, and probably for other formats as well, but this wasn't extensively tested. * Replaced purrr::map with lapply on SMC (#142) * Replaced tidyr calls in SMC functions (#142) - Replaced tidyr::gather with stats::reshape - Replaced tidyr::spreadh with stats::reshape * Minor adjustments to SMC vignette Removed unused dependencies, Removed usage of , automatic whitespace adjustments. * Renamed SMC-Mallows vignette file So it matches the capitalization of "BayesMallows.pdf.asis" * Removed unneeded suggestions from DESCRIPTION * Created generic for compute_consensus * Generalized compute_consensus subfunctions (#80) Tests started failing due to different output structure from the previous expectation. To be investigated. * Fixed leftover merge conflict from develop * Increment version number to 1.0.4.9002 * Dropped usage of dplyr::do * Updated docs * Increment version number to 1.0.4.9003 * Reindented test code to aid debugging * Removed duplicated unit tests with wrong name Apparently git didn't pickup on the renaming of "test_" to "test-" * (Re)deleted redundant bib file All vignettes point to a common inst/REFERENCES.bib, no need for a new file. * sorted the consensus output * Renamed class of compute_consensus.BayesMallows output (#80) * Renamed test file Since it contains tests for both `compute_consensus()` and `compute_posterior_intervals()`. * Added class tests for compute_rho_consensus (#80) * Introducing consensus_SMCMallows class (#80) * Moved compute_consensus methods together (#80) * Updated docs * Removed finished TODOs from code Co-authored-by: Anja Stein Co-authored-by: Anja Stein Co-authored-by: Øystein Sørensen Co-authored-by: Øystein Sørensen Co-authored-by: Anja Stein Co-authored-by: Anja Stein --- DESCRIPTION | 2 +- NAMESPACE | 1 + NEWS.md | 2 + R/compute_consensus.R | 255 ++++++++++++++++-- R/smc_post_processing_functions.R | 201 +------------- man/compute_consensus.BayesMallows.Rd | 38 +++ man/compute_consensus.Rd | 29 +- ...compute_consensus.consensus_SMCMallows.Rd} | 8 +- ...est-classes_compute_consensus_posterior.R} | 12 +- tests/testthat/test-compute_consensus.R | 36 ++- 10 files changed, 319 insertions(+), 265 deletions(-) create mode 100644 man/compute_consensus.BayesMallows.Rd rename man/{compute_consensus_smc.Rd => compute_consensus.consensus_SMCMallows.Rd} (78%) rename tests/testthat/{test-compute_consensus_workflow.R => test-classes_compute_consensus_posterior.R} (91%) diff --git a/DESCRIPTION b/DESCRIPTION index efd0b612..f3cbc2fd 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: BayesMallows Type: Package Title: Bayesian Preference Learning with the Mallows Rank Model -Version: 1.0.4.9001 +Version: 1.0.4.9003 Authors@R: c(person("Oystein", "Sorensen", email = "oystein.sorensen.1985@gmail.com", role = c("aut", "cre"), diff --git a/NAMESPACE b/NAMESPACE index 9588503f..89409b66 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,5 +1,6 @@ # Generated by roxygen2: do not edit by hand +S3method(compute_consensus,BayesMallows) S3method(compute_posterior_intervals,BayesMallows) S3method(compute_posterior_intervals,SMCMallows) S3method(plot,BayesMallows) diff --git a/NEWS.md b/NEWS.md index fbdb85fd..da8b0fa2 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,5 @@ +# BayesMallows (development version) + # BayesMallows 1.0.4.9001 * This is a major update, with new functions for estimating the Bayesian Mallows diff --git a/R/compute_consensus.R b/R/compute_consensus.R index 995471c9..07fe880a 100644 --- a/R/compute_consensus.R +++ b/R/compute_consensus.R @@ -1,37 +1,49 @@ -#' Compute Consensus Ranking -#' -#' Compute the consensus ranking using either cumulative probability (CP) or maximum a posteriori (MAP) consensus +#' @title Compute Consensus Ranking +#' @description Compute the consensus ranking using either cumulative +#' probability (CP) or maximum a posteriori (MAP) consensus #' \insertCite{vitelli2018}{BayesMallows}. For mixture models, the -#' consensus is given for each mixture. Consensus of augmented ranks can also be computed +#' consensus is given for each mixture. Consensus of augmented ranks +#' can also be computed #' for each assessor, by setting \code{parameter = "Rtilde"}. -#' #' @param model_fit An object returned from \code{\link{compute_mallows}}. -#' +#' @param ... other arguments passed to methods. +#' @references \insertAllCited{} +#' @export +#' @example /inst/examples/compute_consensus_example.R +compute_consensus <- function(model_fit, ...) { + UseMethod("compute_consensus") +} + +.compute_cp_consensus <- function(df, ...) { + UseMethod(".compute_cp_consensus") +} + +.compute_map_consensus <- function(df, ...) { + UseMethod(".compute_map_consensus") +} + +find_cpc <- function(df, ...) { + UseMethod("find_cpc") +} + +#' @title Compute Consensus Ranking +#' @inheritParams compute_consensus #' @param type Character string specifying which consensus to compute. Either #' \code{"CP"} or \code{"MAP"}. Defaults to \code{"CP"}. -#' #' @param burnin A numeric value specifying the number of iterations #' to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be #' provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}. -#' #' @param parameter Character string defining the parameter for which to compute the #' consensus. Defaults to \code{"rho"}. Available options are \code{"rho"} and \code{"Rtilde"}, #' with the latter giving consensus rankings for augmented ranks. -#' #' @param assessors When \code{parameter = "rho"}, this integer vector is used to #' define the assessors for which to compute the augmented ranking. Defaults to #' \code{1L}, which yields augmented rankings for assessor 1. -#' -#' @references \insertAllCited{} -#' -#' #' @export -#' -#' @example /inst/examples/compute_consensus_example.R -#' -compute_consensus <- function(model_fit, type = "CP", burnin = model_fit$burnin, - parameter = "rho", assessors = 1L){ - +compute_consensus.BayesMallows <- function( + model_fit, type = "CP", burnin = model_fit$burnin, parameter = "rho", + assessors = 1L, ... +) { if(is.null(burnin)){ stop("Please specify the burnin.") } @@ -40,8 +52,7 @@ compute_consensus <- function(model_fit, type = "CP", burnin = model_fit$burnin, stopifnot(class(model_fit) == "BayesMallows") if(parameter == "Rtilde" && !inherits(model_fit$augmented_data, "data.frame")){ - stop(paste("For augmented ranks, please refit", - "model with option 'save_aug = TRUE'.")) + stop("For augmented ranks, please refit model with option 'save_aug = TRUE'.") } if(parameter == "rho"){ @@ -58,6 +69,8 @@ compute_consensus <- function(model_fit, type = "CP", burnin = model_fit$burnin, # the model object stopifnot(model_fit$n_clusters * model_fit$n_items == n_rows) + class(df) <- c("consensus_BayesMallows", "tbl_df", "tbl", "data.frame") + df <- if(type == "CP"){ .compute_cp_consensus(df) } else if(type == "MAP"){ @@ -81,6 +94,7 @@ compute_consensus <- function(model_fit, type = "CP", burnin = model_fit$burnin, # Treat assessors as clusters df <- dplyr::rename(df, cluster = "assessor") + class(df) <- c("consensus_BayesMallows", "tbl_df", "tbl", "data.frame") df <- if(type == "CP"){ .compute_cp_consensus(df) @@ -96,10 +110,33 @@ compute_consensus <- function(model_fit, type = "CP", burnin = model_fit$burnin, return(df) +} +#' Compute Consensus Ranking +#' +#' Compute the consensus ranking using either cumulative probability (CP) or maximum a posteriori (MAP) consensus +#' \insertCite{vitelli2018}{BayesMallows}. For mixture models, the +#' consensus is given for each mixture. +#' +#' @param model_fit An object returned from \code{\link{compute_mallows}}. +#' +#' @param type Character string specifying which consensus to compute. Either +#' \code{"CP"} or \code{"MAP"}. Defaults to \code{"CP"}. +#' +#' @param burnin A numeric value specifying the number of iterations +#' to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be +#' provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}. +#' @author Anja Stein +#' +compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { + if (type == "CP") { + .compute_cp_consensus(model_fit, burnin = burnin) + } else if (type == "MAP") { + .compute_map_consensus(model_fit, burnin = burnin) + } } -.compute_cp_consensus <- function(df){ +.compute_cp_consensus.consensus_BayesMallows <- function(df){ # Convert items and cluster to character, since factor levels are not needed in this case df <- dplyr::mutate_at(df, dplyr::vars(.data$item, .data$cluster), @@ -123,9 +160,13 @@ compute_consensus <- function(model_fit, type = "CP", burnin = model_fit$burnin, # Find the CP consensus per cluster, using the find_cpc function df <- dplyr::ungroup(df) df <- dplyr::group_by(df, .data$cluster) - df <- dplyr::do(df, find_cpc(.data)) + class(df) <- c("consensus_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") + df <- find_cpc(df) df <- dplyr::ungroup(df) + df <- dplyr::arrange(df, .data$cluster, .data$ranking) + + # If there is only one cluster, we drop the cluster column if(length(unique(df$cluster)) == 1){ df <- dplyr::select(df, -.data$cluster) @@ -134,9 +175,65 @@ compute_consensus <- function(model_fit, type = "CP", burnin = model_fit$burnin, return(df) } +.compute_cp_consensus.consensus_SMCMallows <- function(model_fit, burnin){ + if(is.null(burnin)){ + stop("Please specify the burnin.") + } + + stopifnot(burnin < model_fit$nmc) + + # Filter out the pre-burnin iterations + + if(burnin!=0){ + df <- dplyr::filter(model_fit, .data$iteration > burnin) + }else {df <- model_fit} + + # Find the problem dimensions + n_rows <- nrow(dplyr::distinct(df, .data$item, .data$cluster)) + + # Check that there are rows. + stopifnot(n_rows > 0) + + # Check that the number of rows are consistent with the information in + # the model object + stopifnot(model_fit$n_clusters * model_fit$n_items == n_rows) + + # Convert items and clustr to character, since factor levels are not needed in this case + df <- dplyr::mutate_at(df, dplyr::vars(.data$item, .data$cluster), as.character) + + # Group by item, cluster, and value + df <- dplyr::group_by(df, .data$item, .data$cluster, .data$value) + + # Find the count of each unique combination (value, item, cluster) + df <- dplyr::count(df) + + # Arrange according to value, per item and cluster + df <- dplyr::ungroup(df) + df <- dplyr::group_by(df, .data$item, .data$cluster) + df <- dplyr::arrange(df, .data$value, .by_group = TRUE) + + # Find the cumulative probability, by dividing by the total + # count in (item, cluster) and the summing cumulatively + df <- dplyr::mutate(df, cumprob = cumsum(.data$n/sum(.data$n))) + + # Find the CP consensus per cluster, using the find_cpc function + df <- dplyr::ungroup(df) + df <- dplyr::group_by(df, .data$cluster) + class(df) <- c("consensus_SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame") + df <- find_cpc(df) + df <- dplyr::ungroup(df) + + # If there is only one cluster, we drop the cluster column + if (model_fit$n_clusters[1] == 1) { + df <- dplyr::select(df, -.data$cluster) + } + + return(df) + +} # Internal function for finding CP consensus. -find_cpc <- function(group_df){ +find_cpc.consensus_BayesMallows <- function(group_df){ # Declare the result dataframe before adding rows to it result <- dplyr::tibble( cluster = character(), @@ -168,7 +265,45 @@ find_cpc <- function(group_df){ return(result) } -.compute_map_consensus <- function(df){ +# Internal function for finding CP consensus. +find_cpc.consensus_SMCMallows <- function(group_df){ + # Declare the result dataframe before adding rows to it + result <- dplyr::tibble( + cluster = character(), + ranking = numeric(), + item = character(), + cumprob = numeric() + ) + n_items <- max(group_df$value) + for(i in seq(from = 1, to = n_items, by = 1)){ + # Filter out the relevant rows + tmp_df <- dplyr::filter(group_df, group_df$value == i) + + # Remove items in result + tmp_df <- dplyr::anti_join(tmp_df, result, by = c("cluster", "item")) + + # Keep the max only. This filtering must be done after the first filter, + # since we take the maximum among the filtered values + if (nrow(tmp_df) >= 1) { + tmp_df <- dplyr::filter(tmp_df, .data$cumprob == max(.data$cumprob)) + } + + # Add the ranking + tmp_df <- dplyr::mutate(tmp_df, ranking = i) + + # Select the columns we want to keep, and put them in result + result <- dplyr::bind_rows( + result, + dplyr::select( + tmp_df, .data$cluster, .data$ranking, .data$item, .data$cumprob + ) + ) + + } + return(result) +} + +.compute_map_consensus.consensus_BayesMallows <- function(df){ # Store the total number of iterations after burnin n_samples <- length(unique(df$iteration)) @@ -213,6 +348,76 @@ find_cpc <- function(group_df){ + return(df) + +} + + #AS: added one extra line of code to resolve of the issues in #118 with plotting too many rows in compute_rho_consensus +.compute_map_consensus.consensus_SMCMallows <- function(model_fit, burnin = model_fit$burnin){ + if(is.null(burnin)){ + stop("Please specify the burnin.") + } + + if(burnin != 0){ + df <- dplyr::filter(model_fit, .data$iteration > burnin) + } else { + df <- model_fit + } + + # Store the total number of iterations after burnin + n_samples <- length(unique(df$iteration)) + + #----------------------------------------------------------- + #AS: remove the column n_clusters, parameter + df <- within(df, {n_clusters <- NULL; parameter <- NULL}) + #------------------------------------------------------------ + + # Spread to get items along columns + df <- stats::reshape( + data = as.data.frame(df), + direction = "wide", + idvar = c("iteration", "cluster"), + timevar = "item", + varying = list(unique(df$item)) + ) + attr(df, "reshapeWide") <- NULL # maintain identity to spread() output + + # Group by everything except iteration, and count the unique combinations + df <- dplyr::group_by_at(df, .vars = dplyr::vars(-.data$iteration)) + df <- dplyr::count(df) + df <- dplyr::ungroup(df) + # Keep only the maximum per cluster + df <- dplyr::group_by(df, .data$cluster) + df <- dplyr::mutate(df, n_max = max(.data$n)) + df <- dplyr::filter(df, .data$n == .data$n_max) + df <- dplyr::ungroup(df) + + # Compute the probability + df <- dplyr::mutate(df, probability = .data$n / n_samples) + df <- dplyr::select(df, -.data$n_max, -.data$n) + + # Now collect one set of ranks per cluster + df <- stats::reshape( + as.data.frame(df), + direction = "long", + varying = setdiff(names(df), c("cluster", "probability")), + new.row.names = seq_len(prod(dim(df))), + v.names = "map_ranking", + timevar = "item", + idvar = NULL, + times = setdiff(names(df), c("cluster", "probability")) + ) + attr(x = df, "reshapeLong") <- NULL # preserves identity to gather() output + + # Sort according to cluster and ranking + df <- dplyr::arrange(df, .data$cluster, .data$map_ranking) + + if (model_fit$n_clusters[1] == 1) { + df <- dplyr::select(df, -.data$cluster) + } + + df <- dplyr::as_tibble(df) # added to solve issue #163. Remove for # 162. + return(df) } diff --git a/R/smc_post_processing_functions.R b/R/smc_post_processing_functions.R index 73d9c5c9..9a144cd2 100644 --- a/R/smc_post_processing_functions.R +++ b/R/smc_post_processing_functions.R @@ -35,200 +35,6 @@ smc_processing <- function(output, colnames = NULL) { return(new_df) } -#' Compute Consensus Ranking -#' -#' Compute the consensus ranking using either cumulative probability (CP) or maximum a posteriori (MAP) consensus -#' \insertCite{vitelli2018}{BayesMallows}. For mixture models, the -#' consensus is given for each mixture. -#' -#' @param model_fit An object returned from \code{\link{compute_mallows}}. -#' -#' @param type Character string specifying which consensus to compute. Either -#' \code{"CP"} or \code{"MAP"}. Defaults to \code{"CP"}. -#' -#' @param burnin A numeric value specifying the number of iterations -#' to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be -#' provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}. -#' @author Anja Stein -#' -compute_consensus_smc <- function(model_fit, type, burnin) { - if (type == "CP") { - .compute_cp_consensus_smc(model_fit, burnin = burnin) - } else if (type == "MAP") { - .compute_map_consensus_smc(model_fit, burnin = burnin) - } -} - -.compute_cp_consensus_smc <- function(model_fit, burnin){ -#TODO #80: this function already exists on compute_consensus.R. Add S3 method. - - if(is.null(burnin)){ - stop("Please specify the burnin.") - } - - stopifnot(burnin < model_fit$nmc) - - # Filter out the pre-burnin iterations - - if(burnin!=0){ - df <- dplyr::filter(model_fit, .data$iteration > burnin) - }else {df <- model_fit} - - # Find the problem dimensions - n_rows <- nrow(dplyr::distinct(df, .data$item, .data$cluster)) - - # Check that there are rows. - stopifnot(n_rows > 0) - - # Check that the number of rows are consistent with the information in - # the model object - stopifnot(model_fit$n_clusters * model_fit$n_items == n_rows) - - # Convert items and clustr to character, since factor levels are not needed in this case - df <- dplyr::mutate_at(df, dplyr::vars(.data$item, .data$cluster), as.character) - - # Group by item, cluster, and value - df <- dplyr::group_by(df, .data$item, .data$cluster, .data$value) - - # Find the count of each unique combination (value, item, cluster) - df <- dplyr::count(df) - - # Arrange according to value, per item and cluster - df <- dplyr::ungroup(df) - df <- dplyr::group_by(df, .data$item, .data$cluster) - df <- dplyr::arrange(df, .data$value, .by_group = TRUE) - - # Find the cumulative probability, by dividing by the total - # count in (item, cluster) and the summing cumulatively - df <- dplyr::mutate(df, cumprob = cumsum(.data$n/sum(.data$n))) - - # Find the CP consensus per cluster, using the find_cpc_smc function - df <- dplyr::ungroup(df) - df <- dplyr::group_by(df, .data$cluster) - df <- dplyr::do(df, find_cpc_smc(.data)) - df <- dplyr::ungroup(df) - - # If there is only one cluster, we drop the cluster column - if (model_fit$n_clusters[1] == 1) { - df <- dplyr::select(df, -.data$cluster) - } - - return(df) - -} - - -# Internal function for finding CP consensus. -find_cpc_smc <- function(group_df){ -#TODO #80: this function already exists on compute_consensus.R. Add S3 method. - # Declare the result dataframe before adding rows to it - result <- dplyr::tibble( - cluster = character(), - ranking = numeric(), - item = character(), - cumprob = numeric() - ) - n_items <- max(group_df$value) - for(i in seq(from = 1, to = n_items, by = 1)){ - # Filter out the relevant rows - tmp_df <- dplyr::filter(group_df, group_df$value == i) - - # Remove items in result - tmp_df <- dplyr::anti_join(tmp_df, result, by = c("cluster", "item")) - - # Keep the max only. This filtering must be done after the first filter, - # since we take the maximum among the filtered values - if (nrow(tmp_df) >= 1) { - tmp_df <- dplyr::filter(tmp_df, .data$cumprob == max(.data$cumprob)) - } - - # Add the ranking - tmp_df <- dplyr::mutate(tmp_df, ranking = i) - - # Select the columns we want to keep, and put them in result - result <- dplyr::bind_rows( - result, - dplyr::select( - tmp_df, .data$cluster, .data$ranking, .data$item, .data$cumprob - ) - ) - - } - return(result) -} - - #AS: added one extra line of code to resolve of the issues in #118 with plotting too many rows in compute_rho_consensus -.compute_map_consensus_smc <- function(model_fit, burnin = model_fit$burnin){ -#TODO #80: this function already exists on compute_consensus.R. Add S3 method. - - if(is.null(burnin)){ - stop("Please specify the burnin.") - } - - if(burnin != 0){ - df <- dplyr::filter(model_fit, .data$iteration > burnin) - } else { - df <- model_fit - } - - # Store the total number of iterations after burnin - n_samples <- length(unique(df$iteration)) - - #----------------------------------------------------------- - #AS: remove the column n_clusters, parameter - df <- within(df, {n_clusters <- NULL; parameter <- NULL}) - #------------------------------------------------------------ - - # Spread to get items along columns - df <- stats::reshape( - data = as.data.frame(df), - direction = "wide", - idvar = c("iteration", "cluster"), - timevar = "item", - varying = list(unique(df$item)) - ) - attr(df, "reshapeWide") <- NULL # maintain identity to spread() output - - # Group by everything except iteration, and count the unique combinations - df <- dplyr::group_by_at(df, .vars = dplyr::vars(-.data$iteration)) - df <- dplyr::count(df) - df <- dplyr::ungroup(df) - # Keep only the maximum per cluster - df <- dplyr::group_by(df, .data$cluster) - df <- dplyr::mutate(df, n_max = max(.data$n)) - df <- dplyr::filter(df, .data$n == .data$n_max) - df <- dplyr::ungroup(df) - - # Compute the probability - df <- dplyr::mutate(df, probability = .data$n / n_samples) - df <- dplyr::select(df, -.data$n_max, -.data$n) - - # Now collect one set of ranks per cluster - df <- stats::reshape( - as.data.frame(df), - direction = "long", - varying = setdiff(names(df), c("cluster", "probability")), - new.row.names = seq_len(prod(dim(df))), - v.names = "map_ranking", - timevar = "item", - idvar = NULL, - times = setdiff(names(df), c("cluster", "probability")) - ) - attr(x = df, "reshapeLong") <- NULL # preserves identity to gather() output - - # Sort according to cluster and ranking - df <- dplyr::arrange(df, .data$cluster, .data$map_ranking) - - if (model_fit$n_clusters[1] == 1) { - df <- dplyr::select(df, -.data$cluster) - } - - df <- dplyr::as_tibble(df) # added to solve issue #163. Remove for # 162. - - return(df) - -} - #' @title Compute Posterior Intervals Rho #' @description posterior confidence intervals for rho #' @inheritParams smc_processing @@ -293,14 +99,15 @@ compute_rho_consensus <- function(output, nmc, burnin, C, type, colnames = NULL, smc_plot$n_clusters <- C smc_plot$parameter <- "rho" smc_plot$cluster <- "cluster 1" + class(smc_plot) <- c("consensus_SMCMallows", "data.frame") # rho estimation using cumulative probability if (type == "CP") { - results <- compute_consensus_smc( + results <- compute_consensus( model_fit = smc_plot, type = "CP", burnin = burnin ) } else { - results <- compute_consensus_smc( + results <- compute_consensus( model_fit = smc_plot, type = "MAP", burnin = burnin ) } @@ -352,8 +159,6 @@ compute_posterior_intervals_alpha <- function(output, nmc, burnin, verbose=FALSE return(alpha_mixture_posterior_interval) } - - #' @title Plot the posterior for rho for each item #' @param output input #' @param nmc Number of Monte Carlo samples diff --git a/man/compute_consensus.BayesMallows.Rd b/man/compute_consensus.BayesMallows.Rd new file mode 100644 index 00000000..8a6c3d89 --- /dev/null +++ b/man/compute_consensus.BayesMallows.Rd @@ -0,0 +1,38 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/compute_consensus.R +\name{compute_consensus.BayesMallows} +\alias{compute_consensus.BayesMallows} +\title{Compute Consensus Ranking} +\usage{ +\method{compute_consensus}{BayesMallows}( + model_fit, + type = "CP", + burnin = model_fit$burnin, + parameter = "rho", + assessors = 1L, + ... +) +} +\arguments{ +\item{model_fit}{An object returned from \code{\link{compute_mallows}}.} + +\item{type}{Character string specifying which consensus to compute. Either +\code{"CP"} or \code{"MAP"}. Defaults to \code{"CP"}.} + +\item{burnin}{A numeric value specifying the number of iterations +to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be +provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}.} + +\item{parameter}{Character string defining the parameter for which to compute the +consensus. Defaults to \code{"rho"}. Available options are \code{"rho"} and \code{"Rtilde"}, +with the latter giving consensus rankings for augmented ranks.} + +\item{assessors}{When \code{parameter = "rho"}, this integer vector is used to +define the assessors for which to compute the augmented ranking. Defaults to +\code{1L}, which yields augmented rankings for assessor 1.} + +\item{...}{other arguments passed to methods.} +} +\description{ +Compute Consensus Ranking +} diff --git a/man/compute_consensus.Rd b/man/compute_consensus.Rd index 8e7a8a78..626738d4 100644 --- a/man/compute_consensus.Rd +++ b/man/compute_consensus.Rd @@ -4,36 +4,19 @@ \alias{compute_consensus} \title{Compute Consensus Ranking} \usage{ -compute_consensus( - model_fit, - type = "CP", - burnin = model_fit$burnin, - parameter = "rho", - assessors = 1L -) +compute_consensus(model_fit, ...) } \arguments{ \item{model_fit}{An object returned from \code{\link{compute_mallows}}.} -\item{type}{Character string specifying which consensus to compute. Either -\code{"CP"} or \code{"MAP"}. Defaults to \code{"CP"}.} - -\item{burnin}{A numeric value specifying the number of iterations -to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be -provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}.} - -\item{parameter}{Character string defining the parameter for which to compute the -consensus. Defaults to \code{"rho"}. Available options are \code{"rho"} and \code{"Rtilde"}, -with the latter giving consensus rankings for augmented ranks.} - -\item{assessors}{When \code{parameter = "rho"}, this integer vector is used to -define the assessors for which to compute the augmented ranking. Defaults to -\code{1L}, which yields augmented rankings for assessor 1.} +\item{...}{other arguments passed to methods.} } \description{ -Compute the consensus ranking using either cumulative probability (CP) or maximum a posteriori (MAP) consensus +Compute the consensus ranking using either cumulative +probability (CP) or maximum a posteriori (MAP) consensus \insertCite{vitelli2018}{BayesMallows}. For mixture models, the -consensus is given for each mixture. Consensus of augmented ranks can also be computed +consensus is given for each mixture. Consensus of augmented ranks +can also be computed for each assessor, by setting \code{parameter = "Rtilde"}. } \examples{ diff --git a/man/compute_consensus_smc.Rd b/man/compute_consensus.consensus_SMCMallows.Rd similarity index 78% rename from man/compute_consensus_smc.Rd rename to man/compute_consensus.consensus_SMCMallows.Rd index 283b5bba..300ad0dc 100644 --- a/man/compute_consensus_smc.Rd +++ b/man/compute_consensus.consensus_SMCMallows.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/smc_post_processing_functions.R -\name{compute_consensus_smc} -\alias{compute_consensus_smc} +% Please edit documentation in R/compute_consensus.R +\name{compute_consensus.consensus_SMCMallows} +\alias{compute_consensus.consensus_SMCMallows} \title{Compute Consensus Ranking} \usage{ -compute_consensus_smc(model_fit, type, burnin) +\method{compute_consensus}{consensus_SMCMallows}(model_fit, type, burnin) } \arguments{ \item{model_fit}{An object returned from \code{\link{compute_mallows}}.} diff --git a/tests/testthat/test-compute_consensus_workflow.R b/tests/testthat/test-classes_compute_consensus_posterior.R similarity index 91% rename from tests/testthat/test-compute_consensus_workflow.R rename to tests/testthat/test-classes_compute_consensus_posterior.R index 045a5556..7d826c18 100644 --- a/tests/testthat/test-compute_consensus_workflow.R +++ b/tests/testthat/test-classes_compute_consensus_posterior.R @@ -110,11 +110,17 @@ context("compute_consensus() classes") fit_bm_consensus_cp <- compute_consensus(fit_bm, type = "CP") fit_bm_consensus_map <- compute_consensus(fit_bm, type = "MAP") -# TODO #80: add tests for SMC (compute_rho_consensus). Read -# https://github.com/ocbe-uio/BayesMallows/issues/80#issuecomment-984444753 for -# details +fit_smc_rho <- fit_smc$rho_samples[, , Time + 1] +fit_smc_consensus_cp <- compute_rho_consensus( + output = fit_smc_rho, nmc = nmc, burnin = 0, C = 1, type = "CP" +) +fit_smc_consensus_map <- compute_rho_consensus( + output = fit_smc_rho, nmc = nmc, burnin = 0, C = 1, type = "MAP" +) test_that("Classes are correctly attributed", { expect_s3_class(fit_bm_consensus_cp, "data.frame") expect_s3_class(fit_bm_consensus_map, "data.frame") + expect_s3_class(fit_smc_consensus_cp, "data.frame") + expect_s3_class(fit_smc_consensus_map, "data.frame") }) diff --git a/tests/testthat/test-compute_consensus.R b/tests/testthat/test-compute_consensus.R index d79ea32c..e3ab410a 100644 --- a/tests/testthat/test-compute_consensus.R +++ b/tests/testthat/test-compute_consensus.R @@ -98,21 +98,35 @@ test_that("compute_consensus computes augmented ranks correctly", { "Item 2"), cumprob = c(0.61, 1, 1)), row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame")) ) - res <- compute_consensus(b2, type = "CP", burnin = 200, parameter = "Rtilde", assessors = c(3L, 5L)) - expect_equal(res, structure(list(assessor = c("3", "3", "3", "5", "5", "5"), ranking = c(1, - 2, 3, 1, 2, 3), item = c("Item 1", "Item 3", "Item 2", "Item 1", - "Item 3", "Item 2"), cumprob = c(0.696666666666667, 0.836666666666667, - 1, 1, 1, 1)), row.names = c(NA, -6L), class = c("tbl_df", "tbl", - "data.frame")) + res <- compute_consensus(b2, type = "CP", burnin = 200, parameter = "Rtilde", assessors = c(3L, 5L)) + expect_equal( + res, + structure( + list( + assessor = c("3", "3", "3", "5", "5", "5"), + ranking = c(1, 2, 3, 1, 2, 3), + item = c("Item 1", "Item 3", "Item 2", "Item 1", "Item 3", "Item 2"), + cumprob = c(0.696666666666667, 0.836666666666667, 1, 1, 1, 1) + ), + row.names = c(NA, -6L), + class = c("tbl_df", "tbl", "data.frame") + ) ) res <- compute_consensus(b3, type = "CP", burnin = 200, parameter = "Rtilde", assessors = c(3L, 5L)) - expect_equal(res, - structure(list(assessor = c("3", "3", "3", "5", "5", "5"), ranking = c(1, - 2, 3, 1, 2, 3), item = c("Item 1", "Item 3", "Item 2", "Item 1", - "Item 3", "Item 2"), cumprob = c(0.69, 0.84, 1, 1, 1, 1)), row.names = c(NA, - -6L), class = c("tbl_df", "tbl", "data.frame")) + expect_equal( + res, + structure( + list( + assessor = c("3", "3", "3", "5", "5", "5"), + ranking = c(1, 2, 3, 1, 2, 3), + item = c("Item 1", "Item 3", "Item 2", "Item 1", "Item 3", "Item 2"), + cumprob = c(0.69, 0.84, 1, 1, 1, 1) + ), + row.names = c(NA, -6L), + class = c("tbl_df", "tbl", "data.frame") + ) ) res <- compute_consensus(b2, type = "MAP", burnin = 200, parameter = "Rtilde", assessors = 1L) From 528a78d7118e45e44918482bcaf0a71fea73459d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Fri, 3 Dec 2021 17:38:47 +0100 Subject: [PATCH 27/41] incremented version number and updated CRAN note --- DESCRIPTION | 2 +- NEWS.md | 4 +++- cran-comments.md | 4 +++- 3 files changed, 7 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index f3cbc2fd..09b84ab3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: BayesMallows Type: Package Title: Bayesian Preference Learning with the Mallows Rank Model -Version: 1.0.4.9003 +Version: 1.1.0 Authors@R: c(person("Oystein", "Sorensen", email = "oystein.sorensen.1985@gmail.com", role = c("aut", "cre"), diff --git a/NEWS.md b/NEWS.md index da8b0fa2..8f454dc1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,4 +1,6 @@ -# BayesMallows (development version) +# BayesMallows 1.1.0 + +* Major update, introducing a whole new class of methods using sequential Monte Carlo. Also reducing the number of dependencies. # BayesMallows 1.0.4.9001 diff --git a/cran-comments.md b/cran-comments.md index d0878ab9..4390f429 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -1,9 +1,11 @@ ## Resubmission Note -This is a resubmission which fixes several smaller bugs, increases the number of unit tests, and removes the PLMIX package from Imports. Two new authors/contributors have also been added in DESCRIPTION. +This is a resubmission which introduces new sequential Monte Carlo algorithms, describing in a new vignette. In addition, several dependencies have been removed. ## Test Environments * local Windows install, R 4.1.2 * windows, win-devel. +* Apple Silicon (M1) via rhub. +* valgrind and GCC-UBSAN via rhub. * R-CMD-check via GitHub Actions on windows-latest, macOS-latest, ubuntu-20.04 (release), and ubuntu-20.04 (devel). ## R CMD CHECK results From 5429afc2f9c54b5f50081661bfdf65d6c1f3bafe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Fri, 3 Dec 2021 17:40:58 +0100 Subject: [PATCH 28/41] corrected spelling error in smc vignette --- vignettes/SMC-Mallows.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/SMC-Mallows.Rmd b/vignettes/SMC-Mallows.Rmd index d4752e7f..757a4304 100644 --- a/vignettes/SMC-Mallows.Rmd +++ b/vignettes/SMC-Mallows.Rmd @@ -314,7 +314,7 @@ for (ii in (n_items - 1):(n_items / 2)) { } ``` -This means that we now have a 3D array containing 10 partial datasets and the one complete dataset. The third dimension of the array represents artifical time. We can view the updated partial rankings scenario as viewing several 2D slices of the observed dataset sequentially. So you can see, for example, that at the 5th time point, we observe the top-14 items from `potato_visual`. +This means that we now have a 3D array containing 10 partial datasets and the one complete dataset. The third dimension of the array represents artificial time. We can view the updated partial rankings scenario as viewing several 2D slices of the observed dataset sequentially. So you can see, for example, that at the 5th time point, we observe the top-14 items from `potato_visual`. ```{r test_dataset_example, message=FALSE, warning=FALSE} test_dataset[, , 5] From 3801b149aca19b80905b5cfe97870d60d17c630d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Fri, 3 Dec 2021 17:47:35 +0100 Subject: [PATCH 29/41] recompiled documentation after resolving conflicts with master branch --- man/compute_consensus_smc.Rd | 26 -------------------------- 1 file changed, 26 deletions(-) delete mode 100644 man/compute_consensus_smc.Rd diff --git a/man/compute_consensus_smc.Rd b/man/compute_consensus_smc.Rd deleted file mode 100644 index 283b5bba..00000000 --- a/man/compute_consensus_smc.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/smc_post_processing_functions.R -\name{compute_consensus_smc} -\alias{compute_consensus_smc} -\title{Compute Consensus Ranking} -\usage{ -compute_consensus_smc(model_fit, type, burnin) -} -\arguments{ -\item{model_fit}{An object returned from \code{\link{compute_mallows}}.} - -\item{type}{Character string specifying which consensus to compute. Either -\code{"CP"} or \code{"MAP"}. Defaults to \code{"CP"}.} - -\item{burnin}{A numeric value specifying the number of iterations -to discard as burn-in. Defaults to \code{model_fit$burnin}, and must be -provided if \code{model_fit$burnin} does not exist. See \code{\link{assess_convergence}}.} -} -\description{ -Compute the consensus ranking using either cumulative probability (CP) or maximum a posteriori (MAP) consensus -\insertCite{vitelli2018}{BayesMallows}. For mixture models, the -consensus is given for each mixture. -} -\author{ -Anja Stein -} From 4d1a73cac9539cbb7d9b0cab2d07e093509bccaf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Fri, 3 Dec 2021 18:21:38 +0100 Subject: [PATCH 30/41] recompiled readme and update some citations --- inst/CITATION | 1 - inst/REFERENCES.bib | 2 +- man/figures/README-unnamed-chunk-5-1.png | Bin 0 -> 5581 bytes man/figures/README-unnamed-chunk-6-1.png | Bin 0 -> 4761 bytes 4 files changed, 1 insertion(+), 2 deletions(-) create mode 100644 man/figures/README-unnamed-chunk-5-1.png create mode 100644 man/figures/README-unnamed-chunk-6-1.png diff --git a/inst/CITATION b/inst/CITATION index ec4f531c..a7b863d6 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -11,5 +11,4 @@ bibentry(bibtype = "Article", number = "1", pages = "324--342", volume = "12", - url = "https://doi.org/10.32614/RJ-2020-026", year = "2020") diff --git a/inst/REFERENCES.bib b/inst/REFERENCES.bib index bb8a729d..ee43190c 100644 --- a/inst/REFERENCES.bib +++ b/inst/REFERENCES.bib @@ -76,7 +76,7 @@ @article{sorensen2020 title = {{{BayesMallows}}: {{An R Package}} for the {{Bayesian Mallows Model}}}, shorttitle = {{{BayesMallows}}}, author = {S{\o}rensen, {\O}ystein and Crispino, Marta and Liu, Qinghua and Vitelli, Valeria}, - url = {https://doi.org/10.32614/RJ-2020-026}, + doi = {10.32614/RJ-2020-026}, year = {2020}, volume = {12}, pages = {324--342}, diff --git a/man/figures/README-unnamed-chunk-5-1.png b/man/figures/README-unnamed-chunk-5-1.png new file mode 100644 index 0000000000000000000000000000000000000000..c217ba7b65ae27a34f576efd202bcc1e813842e3 GIT binary patch literal 5581 zcmb_gdpJ~G+h2Q+8O#)u94eK~qf$sAa)?za(}CoXl0zkp34=sM4!a)J=qZg3%CS*8 zND?xl=s+2jj6=dWbRg$L49RJ}t-kBKp6h-8c;7$1`?_ZK+H3vpd)@1I-)rr4-BEV7 z)-$Q|r~rVOn;2I10H6>61eYNZJpop_y2RtN-ByPU#1jBh0GPnk6qpkKT)^ZT7#I*; z#JeeAnobMXlq<5C7GE2iXdB;jn`i-ZT8GJ;MlhLNK9ehA@+HKuuZ^#-O|-9Xx^H@G 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zX-hNF5|7S<7mzQduU=#qs3E+NoBkb)Qwo_&H0Zqk-haxhgDAIk?yY&+nDtFjM}iku zlWuu&@rAOa#wy9D2c$cSyN7i*%Am~eZzFN#RS~cJZscpN&sX97K09@3U@7w2DuI7U zp{(Dcb^LdC{nV}H1|B(wn3S_R|MS-a9j3p`Uzurt_)R!}u1ZRSgkE~^(7ZHpzAsU2 zuE8&rg|OWcrpHe_m={K6-XFp>L2%RUdFS`>N%Iuq5^6R6sd6LMFKxZ|jfOq&Jth?r zONBoLZaqChvX@N;;AKbW*N=br`SX-`%YDggjsMMn|Mn|H^k-y7EZrw0|CXOH4T#6d Mev@6PZ9wvW0ie~ Date: Mon, 6 Dec 2021 10:08:30 +0100 Subject: [PATCH 31/41] Added linter CI workflow --- .github/workflows/linter.yml | 71 ++++++++++++++++++++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 .github/workflows/linter.yml diff --git a/.github/workflows/linter.yml b/.github/workflows/linter.yml new file mode 100644 index 00000000..07aafe38 --- /dev/null +++ b/.github/workflows/linter.yml @@ -0,0 +1,71 @@ +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. +# +# See https://github.com/r-lib/actions/tree/master/examples#readme for +# additional example workflows available for the R community. + +# ======================================================== # +# Determines when the action is triggered # +# ======================================================== # + +on: [push, pull_request] +name: linter + +# ======================================================== # +# Determine actions to take # +# ======================================================== # + +jobs: + lint: + runs-on: ubuntu-latest + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + steps: + - name: Checking out the repository + uses: actions/checkout@v2 + + - name: Setting up R + uses: r-lib/actions/setup-r@v1 + with: + use-public-rspm: true + + - name: Installing dependencies + uses: r-lib/actions/setup-r-dependencies@v1 + with: + extra-packages: lintr + + - name: Picking on the coding style + run: | + library(lintr) + excluded_files <- list( + "inst/examples/compute_consensus_example.R", + "inst/examples/compute_mallows_example.R", + "inst/examples/compute_mallows_mixtures_example.R", + "inst/examples/compute_posterior_intervals_example.R", + "inst/examples/estimate_partition_function_example.R", + "inst/examples/generate_constraints_example.R", + "inst/examples/generate_initial_ranking_example.R", + "inst/examples/generate_transitive_closure_example.R", + "inst/examples/label_switching_example.R", + "inst/examples/obs_freq_example.R", + "inst/examples/plot_top_k_example.R", + "inst/examples/plot.BayesMallows_example.R" + ) + style_rules <- list( + absolute_path_linter, assignment_linter, closed_curly_linter, + commas_linter, commented_code_linter, cyclocomp_linter, + equals_na_linter, function_left_parentheses_linter, + infix_spaces_linter, line_length_linter, no_tab_linter, + nonportable_path_linter, object_length_linter, + open_curly_linter, paren_brace_linter, pipe_continuation_linter, + semicolon_terminator_linter, seq_linter, single_quotes_linter, + spaces_inside_linter, spaces_left_parentheses_linter, + T_and_F_symbol_linter, todo_comment_linter, + trailing_blank_lines_linter, trailing_whitespace_linter, + undesirable_function_linter, undesirable_operator_linter, + unneeded_concatenation_linter + ) + lint_package(linters = style_rules, exclusions = excluded_files) + shell: Rscript {0} From 6b53f7e37f128b0caa1d8502eed495a3fa3801f4 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 10:08:50 +0100 Subject: [PATCH 32/41] Using <- for assignment --- R/misc.R | 2 +- R/smc_post_processing_functions.R | 14 ++++---- inst/examples/sample_mallows_example.R | 3 +- .../test-smc_mallows_complete_rankings.R | 36 +++++++++---------- tests/testthat/test-smc_uniform.R | 8 ++--- 5 files changed, 31 insertions(+), 32 deletions(-) diff --git a/R/misc.R b/R/misc.R index 213f5988..07496879 100644 --- a/R/misc.R +++ b/R/misc.R @@ -92,7 +92,7 @@ fill_single_entries <- function (data) { if (is.vector(data)) { data <- t(data) } - K = ncol(data) + K <- ncol(data) r_single_miss <- (rowSums(data == 0) == 1) if (any(r_single_miss)) { w_row <- which(r_single_miss) diff --git a/R/smc_post_processing_functions.R b/R/smc_post_processing_functions.R index 74476e28..4507561d 100644 --- a/R/smc_post_processing_functions.R +++ b/R/smc_post_processing_functions.R @@ -174,30 +174,30 @@ compute_posterior_intervals_alpha <- function(output, nmc, burnin, verbose=FALSE #' @export plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = NULL){ - n_items = dim(output)[2] + n_items <- dim(output)[2] if(is.null(items) && n_items > 5){ message("Items not provided by user or more than 5 items in a ranking. Picking 5 at random.") items <- sample(1:n_items, 5, replace = F) - items = sort(items) + items <- sort(items) } else if (is.null(items) && n_items <= 5) { items <- c(1:n_items) - items = sort(items) + items <- sort(items) } # do smc processing here - smc_plot = smc_processing(output = output, colnames = colnames) + smc_plot <- smc_processing(output = output, colnames = colnames) if(!is.character(items)){ items <- unique(smc_plot$item)[items] } - iteration = rep(c(1:nmc), times = n_items) - df = cbind(iteration, smc_plot) + iteration <- rep(c(1:nmc), times = n_items) + df <- cbind(iteration, smc_plot) if(C==1){ - df = cbind(cluster = "Cluster 1", df) + df <- cbind(cluster = "Cluster 1", df) } df <- dplyr::filter(df, .data$iteration > burnin, .data$item %in% items) diff --git a/inst/examples/sample_mallows_example.R b/inst/examples/sample_mallows_example.R index 2597c5e5..2654a31b 100644 --- a/inst/examples/sample_mallows_example.R +++ b/inst/examples/sample_mallows_example.R @@ -40,10 +40,9 @@ compute_posterior_intervals(model_fit, burnin = 2000, parameter = "alpha") library(PerMallows) # Set the scale parameter of the PerMallows package corresponding to # alpha0 in BayesMallows -theta0 = alpha0 / n_items +theta0 <- alpha0 / n_items # Sample with PerMallows::rmm sample1 <- rmm(n = 100, sigma0 = rho0, theta = theta0, dist.name = "cayley") # Generate the same sample with sample_mallows sample2 <- sample_mallows(rho0 = rho0, alpha0 = alpha0, n_samples = 100, burnin = 1000, thinning = 1000, metric = "cayley") - diff --git a/tests/testthat/test-smc_mallows_complete_rankings.R b/tests/testthat/test-smc_mallows_complete_rankings.R index e784fc9d..1785f066 100755 --- a/tests/testthat/test-smc_mallows_complete_rankings.R +++ b/tests/testthat/test-smc_mallows_complete_rankings.R @@ -5,16 +5,16 @@ context("SMC complete rankings: sequence") ######################### set.seed(994) -data = sushi_rankings[1:100,] +data <- sushi_rankings[1:100,] # General n_items <- dim(sushi_rankings)[2] # Number of items -leap_size = floor(n_items/5) -metric = "footrule" +leap_size <- floor(n_items/5) +metric <- "footrule" # Generate estimate of Z_n(alpha) alpha_vector <- seq(from = 0, to = 15, by = 1) -iter = 1e2 +iter <- 1e2 degree <- 10 # Estimate the logarithm of the partition function of the Mallows rank model using the estimate partition function @@ -27,30 +27,30 @@ logz_estimate <- estimate_partition_function(method = "importance_sampling", ###################################### # BayesMallows Analysis (MCMC) ###################################### -nmc = 20 -burnin=5 +nmc <- 20 +burnin <- 5 model_fit <- compute_mallows(rankings = data, nmc = nmc, metric = metric, leap_size =leap_size, alpha_prop_sd = 0.15, logz_estimate = logz_estimate) -model_fit$burnin = burnin +model_fit$burnin <- burnin -alpha_samples_table = data.frame(iteration = 1:nmc , value = model_fit$alpha$value) -alpha_samples_table = alpha_samples_table[(burnin+1):nmc,] +alpha_samples_table <- data.frame(iteration = 1:nmc , value = model_fit$alpha$value) +alpha_samples_table <- alpha_samples_table[(burnin+1):nmc,] # from observing the plots, this looks like the estimated parameters of the Mallows Model -rho_0 = c(4,5,2,6,8,3,9,1,7,10) -alpha_0 = 1.7 +rho_0 <- c(4,5,2,6,8,3,9,1,7,10) +alpha_0 <- 1.7 # heatplot - there is no burnin! -mcmc_rho_matrix = matrix(model_fit$rho$value, ncol = n_items, nrow = nmc, byrow=TRUE) +mcmc_rho_matrix <- matrix(model_fit$rho$value, ncol = n_items, nrow = nmc, byrow=TRUE) # ################################################################### # # SMC # ################################################################### -mcmc_times = 5 -num_new_obs = 10 -Time = dim(data)[1]/num_new_obs -N = 100 +mcmc_times <- 5 +num_new_obs <- 10 +Time <- dim(data)[1]/num_new_obs +N <- 100 test <- smc_mallows_new_users_complete( R_obs = data, n_items = n_items, metric = metric, @@ -97,11 +97,11 @@ test_that("Output of compute_posterior_intervals_rho is OK", { }) # posterior for alpha -alpha_samples_table = data.frame( +alpha_samples_table <- data.frame( iteration = 1:N , value = test$alpha_samples[,Time+1] ) # posterior confidence intervals -alpha_posterior_intervals = compute_posterior_intervals_alpha( +alpha_posterior_intervals <- compute_posterior_intervals_alpha( output = test$alpha_samples[,Time+1], nmc = N, burnin = 0 ) diff --git a/tests/testthat/test-smc_uniform.R b/tests/testthat/test-smc_uniform.R index e4075bc3..a46176c4 100644 --- a/tests/testthat/test-smc_uniform.R +++ b/tests/testthat/test-smc_uniform.R @@ -5,10 +5,10 @@ require("BayesMallows") # tests for M-H_aug_ranking function =========================================== -rho = c(1,2,3,4,5,6) -alpha = 2 -metric = "footrule" -n_items= 6 +rho <- c(1,2,3,4,5,6) +alpha <- 2 +metric <- "footrule" +n_items <- 6 test_that("MH-aug ranking works", { From 39f4080e473bc5de7d570eb5990a8a05ff63e8dd Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 10:13:44 +0100 Subject: [PATCH 33/41] Removed commented out code --- R/misc_expected_dist.R | 10 ---------- tests/testthat/test-smc_pseudolikelihood.R | 8 ++------ 2 files changed, 2 insertions(+), 16 deletions(-) diff --git a/R/misc_expected_dist.R b/R/misc_expected_dist.R index d619999a..3d92088c 100644 --- a/R/misc_expected_dist.R +++ b/R/misc_expected_dist.R @@ -53,12 +53,6 @@ exp_d_ham <- function(alpha,n_items){ #/' @return Expected value of the Ulam metric under the Mallows rank model with the Ulam distance. # The function based on the command from the PerMallows package is slow because it has to generate the distance frequencies. - -# exp_d_ulam_old=function(alpha,n_items){ -# out=PerMallows::expectation.mm(theta=alpha,perm.length=n_items,dist.name="ulam") -# return(out) -# } - exp_d_ulam <- function(alpha,n_items){ # for n_items<=95 idx <- seq(from = 0, to = n_items - 1, by = 1) pfd <- partition_function_data @@ -84,8 +78,6 @@ exp_d_foot <- function(alpha,n_items){ # for n_items<=50 norm_const <- exp(get_partition_function(alpha=alpha*n_items,n_items=n_items, metric="footrule", cardinalities=card)) - # print(length(idx)) - # print(length(card)) out <- sum(idx*exp(-alpha*idx)*card)/norm_const return(out) } @@ -104,8 +96,6 @@ exp_d_spear <- function(alpha,n_items){ # for n_items<=14 norm_const <- exp(get_partition_function(alpha=alpha*n_items,n_items=n_items, metric="spearman", cardinalities=card)) - # print(length(idx)) - # print(length(card)) out <- sum(idx*exp(-alpha*idx)*card)/norm_const return(out) } diff --git a/tests/testthat/test-smc_pseudolikelihood.R b/tests/testthat/test-smc_pseudolikelihood.R index 3790f744..3fd78ef8 100644 --- a/tests/testthat/test-smc_pseudolikelihood.R +++ b/tests/testthat/test-smc_pseudolikelihood.R @@ -41,7 +41,7 @@ test_1_forward <- calculate_forward_probability( item_ordering = item_ordering, partial_ranking = partial_ranking, remaining_set = remaining_set, rho = rho, alpha = alpha, n_items = n_items, metric = metric -) # TODO #116: get this to output aug_ranking == c(1, 2, 3, 6, 5, 4). +) # Tried all combinations of item_ordering. No dice. current_ranking <- c(1, 2, 6, 5, 4, 3) @@ -52,12 +52,8 @@ test_1_backward_a <- calculate_backward_probability( alpha = alpha, n_items = n_items, metric = metric ) -new_current_ranking <- test_1_forward$aug_ranking # c(1, 2, 3, 4, 6, 5) +new_current_ranking <- test_1_forward$aug_ranking -# new_current_ranking needs to be one of the following so that test_1_backward_b -# equals test_1_forward$forward_prob: -# - c(1, 2, 3, 6, 5, 4) -# - c(1, 2, 6, 4, 3, 5) test_1_backward_b <- calculate_backward_probability( item_ordering = item_ordering, partial_ranking = partial_ranking, current_ranking = new_current_ranking, remaining_set = remaining_set, rho = rho, From bacc949b943693cdac9980fc1783d9ec61bda49a Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 10:30:23 +0100 Subject: [PATCH 34/41] Improved spacing around commas and operators --- R/compute_consensus.R | 10 ++- R/expected_dist.R | 16 ++-- R/misc.R | 16 ++-- R/misc_expected_dist.R | 74 +++++++++++-------- R/misc_likelihood.R | 15 +++- R/plot_top_k.R | 2 +- R/predict_top_k.R | 2 +- R/rank_freq_distr.R | 2 +- R/sample_mallows.R | 4 +- R/smc_post_processing_functions.R | 4 +- inst/examples/expected_dist_example.R | 12 +-- inst/examples/lik_db_mix_example.R | 17 ++--- .../metropolis_hastings_alpha_example.R | 2 +- inst/examples/rank_distance_example.R | 2 +- inst/examples/rank_freq_distr_example.R | 4 +- man/expected_dist.Rd | 12 +-- man/lik_db_mix.Rd | 9 +-- man/metropolis_hastings_alpha.Rd | 2 +- man/rank_distance.Rd | 2 +- man/sample_mallows.Rd | 3 +- tests/testthat/test-compute_mallows.R | 10 +-- tests/testthat/test-distance_function.R | 2 +- tests/testthat/test-expected_dist.R | 12 +-- tests/testthat/test-generate_ranking.R | 7 +- tests/testthat/test-lik_db_mix.R | 18 ++--- tests/testthat/test-misc_functions.R | 2 +- tests/testthat/test-plot.R | 2 +- .../testthat/test-smc_individual_functions.R | 38 +++++----- .../test-smc_mallows_complete_rankings.R | 40 +++++----- .../test-smc_mallows_partial_rankings.R | 12 +-- tests/testthat/test-smc_uniform.R | 2 +- tests/testthat/test-transitive_closure.R | 6 +- 32 files changed, 189 insertions(+), 172 deletions(-) diff --git a/R/compute_consensus.R b/R/compute_consensus.R index 07fe880a..f064cc31 100644 --- a/R/compute_consensus.R +++ b/R/compute_consensus.R @@ -155,7 +155,7 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { # Find the cumulative probability, by dividing by the total # count in (item, cluster) and the summing cumulatively - df <- dplyr::mutate(df, cumprob = cumsum(.data$n/sum(.data$n))) + df <- dplyr::mutate(df, cumprob = cumsum(.data$n / sum(.data$n))) # Find the CP consensus per cluster, using the find_cpc function df <- dplyr::ungroup(df) @@ -184,9 +184,11 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { # Filter out the pre-burnin iterations - if(burnin!=0){ + if(burnin != 0){ df <- dplyr::filter(model_fit, .data$iteration > burnin) - }else {df <- model_fit} + } else { + df <- model_fit + } # Find the problem dimensions n_rows <- nrow(dplyr::distinct(df, .data$item, .data$cluster)) @@ -214,7 +216,7 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { # Find the cumulative probability, by dividing by the total # count in (item, cluster) and the summing cumulatively - df <- dplyr::mutate(df, cumprob = cumsum(.data$n/sum(.data$n))) + df <- dplyr::mutate(df, cumprob = cumsum(.data$n / sum(.data$n))) # Find the CP consensus per cluster, using the find_cpc function df <- dplyr::ungroup(df) diff --git a/R/expected_dist.R b/R/expected_dist.R index 65112db6..130f8b84 100644 --- a/R/expected_dist.R +++ b/R/expected_dist.R @@ -10,7 +10,7 @@ #' #' @example /inst/examples/expected_dist_example.R -expected_dist <- function(alpha,n_items,metric){ +expected_dist <- function(alpha, n_items, metric){ if(n_items < 1 | floor(n_items) != n_items){ stop("Number of items must be a positive integer") @@ -22,16 +22,16 @@ expected_dist <- function(alpha,n_items,metric){ if(alpha < 0){ stop("alpha must be a non-negative value") }else{ - if(metric=="kendall"){ - out <- exp_d_tau(alpha,n_items) + if(metric == "kendall"){ + out <- exp_d_tau(alpha, n_items) } - if(metric=="cayley"){ - out <- exp_d_cay(alpha,n_items) + if(metric == "cayley"){ + out <- exp_d_cay(alpha, n_items) } - if(metric=="hamming"){ - out <- exp_d_ham(alpha,n_items) + if(metric == "hamming"){ + out <- exp_d_ham(alpha, n_items) } - if(metric%in%c("ulam","footrule","spearman")){ + if(metric %in% c("ulam", "footrule", "spearman")){ pfd <- dplyr::filter(partition_function_data, .data$metric == !!metric, .data$n_items == !!n_items, .data$type == "cardinalities") diff --git a/R/misc.R b/R/misc.R index 07496879..ce8c2461 100644 --- a/R/misc.R +++ b/R/misc.R @@ -4,7 +4,7 @@ NULL -.onUnload <- function (libpath) { +.onUnload <- function(libpath) { library.dynam.unload("BayesMallows", libpath) } @@ -12,7 +12,7 @@ NULL #' @param vec a vector #' @return TRUE if vec is a permutation #' @keywords internal -validate_permutation <- function(vec){ +validate_permutation <- function(vec) { if(!any(is.na(vec))){ return(all(sort(vec) == seq_along(vec))) } else if(all(is.na(vec))){ @@ -74,7 +74,7 @@ prepare_partition_function <- function(logz_estimate, metric, n_items){ # function taken from PLMIX package: # Copyright Cristina Mollica and Luca Tardella -unit_to_freq <- function (data) { +unit_to_freq <- function(data) { data <- fill_single_entries(data = data) K <- ncol(data) freq <- table(apply(data, 1, paste, collapse = "-")) @@ -88,7 +88,7 @@ unit_to_freq <- function (data) { # function taken from PLMIX package: # Copyright Cristina Mollica and Luca Tardella -fill_single_entries <- function (data) { +fill_single_entries <- function(data) { if (is.vector(data)) { data <- t(data) } @@ -110,14 +110,14 @@ fill_single_entries <- function (data) { ## Source: https://stackoverflow.com/questions/11095992/generating-all-distinct-permutations-of-a-list-in-r permutations <- function(n){ - if(n==1){ + if(n == 1){ return(matrix(1)) } else { - sp <- permutations(n-1) + sp <- permutations(n - 1) p <- nrow(sp) - A <- matrix(nrow=n*p,ncol=n) + A <- matrix(nrow = n * p, ncol = n) for(i in 1:n){ - A[(i-1)*p+1:p,] <- cbind(i,sp+(sp>=i)) + A[(i - 1) * p + 1:p, ] <- cbind(i, sp + (sp >= i)) } return(A) } diff --git a/R/misc_expected_dist.R b/R/misc_expected_dist.R index 3d92088c..705e899d 100644 --- a/R/misc_expected_dist.R +++ b/R/misc_expected_dist.R @@ -5,14 +5,15 @@ #/' #/' @return Expected value of the Kendall metric under the Mallows rank model with the Kendall distance. -exp_d_tau <- function(alpha,n_items){ - if(alpha>0){ +exp_d_tau <- function(alpha, n_items){ + if(alpha > 0){ idx <- seq(from = 1, to = n_items, by = 1) - out <- n_items*exp(-alpha)/(1-exp(-alpha))-sum((idx*exp(-idx*alpha))/(1-exp(-idx*alpha))) - }else{ - if(alpha==0){ - out <- n_items*(n_items-1)/4 - }else{ + out <- n_items * exp(-alpha) / (1 - exp(-alpha)) - + sum((idx * exp(-idx * alpha)) / (1 - exp(-idx * alpha))) + } else { + if(alpha == 0) { + out <- n_items * (n_items - 1) / 4 + } else { stop("alpha must be a non-negative value") } } @@ -26,9 +27,9 @@ exp_d_tau <- function(alpha,n_items){ #/' #/' @return Expected value of the Cayley metric under the Mallows rank model with the Cayley distance. -exp_d_cay <- function(alpha,n_items){ +exp_d_cay <- function(alpha, n_items){ idx <- seq(from = 1, to = n_items - 1, by = 1) - out <- sum(idx/(idx+exp(alpha))) + out <- sum(idx / (idx + exp(alpha))) return(out) } @@ -39,9 +40,11 @@ exp_d_cay <- function(alpha,n_items){ #/' #/' @return Expected value of the Hamming metric under the Mallows rank model with the Hamming distance. -exp_d_ham <- function(alpha,n_items){ +exp_d_ham <- function(alpha, n_items){ idx <- seq(from = 0, to = n_items, by = 1) - out <- n_items-exp(alpha)*sum(((exp(alpha)-1)^idx[-(n_items+1)])/base::factorial(idx[-(n_items+1)]))/sum(((exp(alpha)-1)^idx)/base::factorial(idx)) + out <- n_items - exp(alpha) * + sum(((exp(alpha) - 1)^idx[-(n_items + 1)]) / base::factorial(idx[-(n_items + 1)])) / + sum(((exp(alpha) - 1)^idx) / base::factorial(idx)) return(out) } @@ -53,14 +56,17 @@ exp_d_ham <- function(alpha,n_items){ #/' @return Expected value of the Ulam metric under the Mallows rank model with the Ulam distance. # The function based on the command from the PerMallows package is slow because it has to generate the distance frequencies. -exp_d_ulam <- function(alpha,n_items){ # for n_items<=95 +exp_d_ulam <- function(alpha, n_items){ # for n_items<=95 idx <- seq(from = 0, to = n_items - 1, by = 1) pfd <- partition_function_data - card <- pfd$values[pfd$metric=="ulam"][[n_items]] - norm_const <- exp(get_partition_function(alpha=alpha*n_items,n_items=n_items, - metric="ulam", - cardinalities=card)) - out <- sum(idx*exp(-alpha*idx)*card)/norm_const + card <- pfd$values[pfd$metric == "ulam"][[n_items]] + norm_const <- exp( + get_partition_function( + alpha = alpha * n_items, n_items = n_items, metric = "ulam", + cardinalities = card + ) + ) + out <- sum(idx * exp(-alpha * idx) * card) / norm_const return(out) } @@ -71,14 +77,17 @@ exp_d_ulam <- function(alpha,n_items){ # for n_items<=95 #/' #/' @return Expected value of the Footrule metric under the Mallows rank model with the Footrule distance. -exp_d_foot <- function(alpha,n_items){ # for n_items<=50 - idx <- seq(0,floor(n_items^2/2),by=2) +exp_d_foot <- function(alpha, n_items){ # for n_items<=50 + idx <- seq(0, floor(n_items^2 / 2), by = 2) pfd <- partition_function_data - card <- pfd$values[pfd$metric=="footrule"][[n_items]] - norm_const <- exp(get_partition_function(alpha=alpha*n_items,n_items=n_items, - metric="footrule", - cardinalities=card)) - out <- sum(idx*exp(-alpha*idx)*card)/norm_const + card <- pfd$values[pfd$metric == "footrule"][[n_items]] + norm_const <- exp( + get_partition_function( + alpha = alpha * n_items, n_items = n_items, metric = "footrule", + cardinalities = card + ) + ) + out <- sum(idx * exp(-alpha * idx) * card) / norm_const return(out) } @@ -89,13 +98,16 @@ exp_d_foot <- function(alpha,n_items){ # for n_items<=50 #/' #/' @return Expected value of the Spearman metric under the Mallows rank model with the Spearman distance. -exp_d_spear <- function(alpha,n_items){ # for n_items<=14 - idx <- seq(0,2*base::choose(n_items+1,3),by=2) +exp_d_spear <- function(alpha, n_items) { # for n_items<=14 + idx <- seq(0, 2 * base::choose(n_items + 1, 3), by = 2) pfd <- partition_function_data - card <- pfd$values[pfd$metric=="spearman"][[n_items]] - norm_const <- exp(get_partition_function(alpha=alpha*n_items,n_items=n_items, - metric="spearman", - cardinalities=card)) - out <- sum(idx*exp(-alpha*idx)*card)/norm_const + card <- pfd$values[pfd$metric == "spearman"][[n_items]] + norm_const <- exp( + get_partition_function( + alpha = alpha * n_items, n_items = n_items, metric = "spearman", + cardinalities = card + ) + ) + out <- sum(idx * exp(-alpha * idx) * card) / norm_const return(out) } diff --git a/R/misc_likelihood.R b/R/misc_likelihood.R index a941e819..4809d78f 100644 --- a/R/misc_likelihood.R +++ b/R/misc_likelihood.R @@ -17,7 +17,16 @@ log_lik_db <- function(rho, alpha, metric, rankings, obs_freq){ n_items <- ncol(rankings) if(metric %in% c("kendall", "cayley", "hamming")){ - log_lik <- -(alpha*rank_dist_sum(rankings=t(rankings),rho=rho,metric=metric,obs_freq=obs_freq)+N*get_partition_function(n_items=n_items,alpha=alpha*n_items,metric=metric)) + log_lik <- -( + alpha * + rank_dist_sum( + rankings = t(rankings), rho = rho, metric = metric, obs_freq = obs_freq + ) + + N * + get_partition_function( + n_items = n_items, alpha = alpha * n_items, metric = metric + ) + ) } if(metric %in% c( "ulam", "footrule", "spearman")){ @@ -62,7 +71,7 @@ log_lik_db <- function(rho, alpha, metric, rankings, obs_freq){ #/' log_lik_db_mix <- function(rho, alpha, weights, metric, - rankings,obs_freq){ + rankings, obs_freq){ L <- length(obs_freq) n_clusters <- length(weights) @@ -75,6 +84,6 @@ log_lik_db_mix <- function(rho, alpha, weights, metric, obs_freq = obs_freq[l])) } } - log_lik <- sum(log(weights%*%temp)) + log_lik <- sum(log(weights %*% temp)) return(log_lik) } diff --git a/R/plot_top_k.R b/R/plot_top_k.R index 0c32d496..71f6f5dc 100644 --- a/R/plot_top_k.R +++ b/R/plot_top_k.R @@ -35,7 +35,7 @@ plot_top_k <- function(model_fit, burnin = model_fit$burnin, # Factors are not needed in this case rho <- dplyr::mutate(rho, item = as.character(.data$item)) rho <- dplyr::group_by(rho, .data$item, .data$cluster) - rho <- dplyr::summarise(rho, prob = dplyr::n()/n_samples, .groups = "drop") + rho <- dplyr::summarise(rho, prob = dplyr::n() / n_samples, .groups = "drop") # Find the complete set of items per cluster rho <- do.call(rbind, lapply(split(rho, f = rho$cluster), function(dd){ diff --git a/R/predict_top_k.R b/R/predict_top_k.R index 5a8f1daa..3eb96eeb 100644 --- a/R/predict_top_k.R +++ b/R/predict_top_k.R @@ -37,7 +37,7 @@ predict_top_k <- function(model_fit, burnin = model_fit$burnin, n_samples <- length(unique(rankings$iteration)) rankings <- dplyr::mutate(rankings, item = as.character(.data$item)) rankings <- dplyr::group_by(rankings, .data$assessor, .data$item) - rankings <- dplyr::summarise(rankings, prob = dplyr::n()/n_samples, .groups = "drop") + rankings <- dplyr::summarise(rankings, prob = dplyr::n() / n_samples, .groups = "drop") do.call(rbind, lapply(split(rankings, f = rankings$assessor), function(dd){ dd2 <- merge(dd, expand.grid(item = unique(rankings$item)), diff --git a/R/rank_freq_distr.R b/R/rank_freq_distr.R index f211f567..5904986e 100644 --- a/R/rank_freq_distr.R +++ b/R/rank_freq_distr.R @@ -21,7 +21,7 @@ rank_freq_distr <- function(rankings){ rankings[is.na(rankings)] <- 0 out <- unit_to_freq(data = rankings) - out[out==0] <- NA + out[out == 0] <- NA return(out) diff --git a/R/sample_mallows.R b/R/sample_mallows.R index 7ae54070..e9a114a6 100644 --- a/R/sample_mallows.R +++ b/R/sample_mallows.R @@ -42,7 +42,7 @@ #' @example /inst/examples/sample_mallows_example.R #' sample_mallows <- function(rho0, alpha0, n_samples, - leap_size = max(1L, floor(n_items/5)), + leap_size = max(1L, floor(n_items / 5)), metric = "footrule", diagnostic = FALSE, burnin = ifelse(diagnostic, 0, 1000), @@ -93,7 +93,7 @@ sample_mallows <- function(rho0, alpha0, n_samples, } # Compute the autocorrelation in the samples - autocorr <- apply(samples[ , items_to_plot, drop = FALSE], 2, stats::acf, + autocorr <- apply(samples[, items_to_plot, drop = FALSE], 2, stats::acf, lag.max = max_lag, plot = FALSE, demean = TRUE) names(autocorr) <- items_to_plot diff --git a/R/smc_post_processing_functions.R b/R/smc_post_processing_functions.R index 4507561d..9a86c14f 100644 --- a/R/smc_post_processing_functions.R +++ b/R/smc_post_processing_functions.R @@ -131,7 +131,7 @@ compute_rho_consensus <- function(output, nmc, burnin, C, type, colnames = NULL, plot_alpha_posterior <- function(output, nmc, burnin) { alpha_samples_table <- data.frame(iteration = 1:nmc, value = output) - plot_posterior_alpha <- ggplot2::ggplot(alpha_samples_table, ggplot2::aes_(x =~ value)) + + plot_posterior_alpha <- ggplot2::ggplot(alpha_samples_table, ggplot2::aes_(x = ~ value)) + ggplot2::geom_density() + ggplot2::xlab(expression(alpha)) + ggplot2::ylab("Posterior density") + @@ -196,7 +196,7 @@ plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = iteration <- rep(c(1:nmc), times = n_items) df <- cbind(iteration, smc_plot) - if(C==1){ + if(C == 1){ df <- cbind(cluster = "Cluster 1", df) } diff --git a/inst/examples/expected_dist_example.R b/inst/examples/expected_dist_example.R index 5ffcd8d9..56a7b763 100644 --- a/inst/examples/expected_dist_example.R +++ b/inst/examples/expected_dist_example.R @@ -1,6 +1,6 @@ -expected_dist(1,5,metric="kendall") -expected_dist(2,6,metric="cayley") -expected_dist(1.5,7,metric="hamming") -expected_dist(5,30,"ulam") -expected_dist(3.5,45,"footrule") -expected_dist(4,10,"spearman") +expected_dist(1, 5, metric = "kendall") +expected_dist(2, 6, metric = "cayley") +expected_dist(1.5, 7, metric = "hamming") +expected_dist(5, 30, "ulam") +expected_dist(3.5, 45, "footrule") +expected_dist(4, 10, "spearman") diff --git a/inst/examples/lik_db_mix_example.R b/inst/examples/lik_db_mix_example.R index 8d6dd901..48476420 100644 --- a/inst/examples/lik_db_mix_example.R +++ b/inst/examples/lik_db_mix_example.R @@ -5,13 +5,13 @@ mydata <- sample_mallows( n_samples = 100, rho0 = 1:n_items, alpha0 = 10, - metric="kendall") + metric = "kendall") # Compute the likelihood and log-likelihood values under the true model... lik_db_mix( - rho = rbind(1:n_items,1:n_items), + rho = rbind(1:n_items, 1:n_items), alpha = c(10, 10), - weights = c(0.5,0.5), + weights = c(0.5, 0.5), metric = "kendall", rankings = mydata ) @@ -28,21 +28,20 @@ lik_db_mix( # or equivalently, by using the frequency distribution freq_distr <- rank_freq_distr(mydata) lik_db_mix( - rho = rbind(1:n_items,1:n_items), + rho = rbind(1:n_items, 1:n_items), alpha = c(10, 10), weights = c(0.5, 0.5), metric = "kendall", rankings = freq_distr[, 1:n_items], - obs_freq = freq_distr[,n_items+1] + obs_freq = freq_distr[, n_items + 1] ) lik_db_mix( rho = rbind(1:n_items, 1:n_items), alpha = c(10, 10), - weights=c(0.5, 0.5), + weights = c(0.5, 0.5), metric = "kendall", rankings = freq_distr[, 1:n_items], - obs_freq = freq_distr[, n_items+1], - log=TRUE + obs_freq = freq_distr[, n_items + 1], + log = TRUE ) - diff --git a/inst/examples/metropolis_hastings_alpha_example.R b/inst/examples/metropolis_hastings_alpha_example.R index 76117b2b..1d742cb5 100644 --- a/inst/examples/metropolis_hastings_alpha_example.R +++ b/inst/examples/metropolis_hastings_alpha_example.R @@ -1,4 +1,4 @@ -rho <- c(1,2,3,4,5,6) +rho <- c(1, 2, 3, 4, 5, 6) alpha <- 2 metric <- "footrule" n_items <- 6 diff --git a/inst/examples/rank_distance_example.R b/inst/examples/rank_distance_example.R index d0d4b1af..d7358e48 100644 --- a/inst/examples/rank_distance_example.R +++ b/inst/examples/rank_distance_example.R @@ -1,6 +1,6 @@ # Distance between two vectors of rankings: -rank_distance(1:5,5:1, metric = "kendall") +rank_distance(1:5, 5:1, metric = "kendall") rank_distance(c(2, 4, 3, 6, 1, 7, 5), c(3, 5, 4, 7, 6, 2, 1), metric = "cayley") rank_distance(c(4, 2, 3, 1), c(3, 4, 1, 2), metric = "hamming") rank_distance(c(1, 3, 5, 7, 9, 8, 6, 4, 2), c(1, 2, 3, 4, 9, 8, 7, 6, 5), "ulam") diff --git a/inst/examples/rank_freq_distr_example.R b/inst/examples/rank_freq_distr_example.R index 24f93203..afc93aba 100644 --- a/inst/examples/rank_freq_distr_example.R +++ b/inst/examples/rank_freq_distr_example.R @@ -1,6 +1,6 @@ # Create example data. We set the burn-in and thinning very low # for the sampling to go fast -data0 <- sample_mallows(rho0 = 1:5, alpha=10, n_samples = 1000, +data0 <- sample_mallows(rho0 = 1:5, alpha = 10, n_samples = 1000, burnin = 10, thinning = 1) # Find the frequency distribution -rank_freq_distr(rankings=data0) +rank_freq_distr(rankings = data0) diff --git a/man/expected_dist.Rd b/man/expected_dist.Rd index 2793c269..caa6aa8a 100644 --- a/man/expected_dist.Rd +++ b/man/expected_dist.Rd @@ -20,10 +20,10 @@ A scalar providing the expected value of the \code{metric} under the Mallows ran Compute the expectation of several metrics under the Mallows rank model. } \examples{ -expected_dist(1,5,metric="kendall") -expected_dist(2,6,metric="cayley") -expected_dist(1.5,7,metric="hamming") -expected_dist(5,30,"ulam") -expected_dist(3.5,45,"footrule") -expected_dist(4,10,"spearman") +expected_dist(1, 5, metric = "kendall") +expected_dist(2, 6, metric = "cayley") +expected_dist(1.5, 7, metric = "hamming") +expected_dist(5, 30, "ulam") +expected_dist(3.5, 45, "footrule") +expected_dist(4, 10, "spearman") } diff --git a/man/lik_db_mix.Rd b/man/lik_db_mix.Rd index d1902fa3..f8ea229c 100644 --- a/man/lik_db_mix.Rd +++ b/man/lik_db_mix.Rd @@ -55,9 +55,9 @@ mydata <- sample_mallows( # Compute the likelihood and log-likelihood values under the true model... lik_db_mix( - rho = rbind(1:n_items,1:n_items), + rho = rbind(1:n_items, 1:n_items), alpha = c(10, 10), - weights = c(0.5,0.5), + weights = c(0.5, 0.5), metric = "kendall", rankings = mydata ) @@ -74,12 +74,12 @@ lik_db_mix( # or equivalently, by using the frequency distribution freq_distr <- rank_freq_distr(mydata) lik_db_mix( - rho = rbind(1:n_items,1:n_items), + rho = rbind(1:n_items, 1:n_items), alpha = c(10, 10), weights = c(0.5, 0.5), metric = "kendall", rankings = freq_distr[, 1:n_items], - obs_freq = freq_distr[,n_items+1] + obs_freq = freq_distr[, n_items + 1] ) lik_db_mix( @@ -91,5 +91,4 @@ lik_db_mix( obs_freq = freq_distr[, n_items+1], log=TRUE ) - } diff --git a/man/metropolis_hastings_alpha.Rd b/man/metropolis_hastings_alpha.Rd index ea9d14a1..72a9ce1a 100644 --- a/man/metropolis_hastings_alpha.Rd +++ b/man/metropolis_hastings_alpha.Rd @@ -60,7 +60,7 @@ Function to perform Metropolis-Hastings for new rho under Alternatively, if \eqn{N} equals 1, \code{rankings} can be a vector. } \examples{ -rho <- c(1,2,3,4,5,6) +rho <- c(1, 2, 3, 4, 5, 6) alpha <- 2 metric <- "footrule" n_items <- 6 diff --git a/man/rank_distance.Rd b/man/rank_distance.Rd index 1a117ff1..21f67ed5 100644 --- a/man/rank_distance.Rd +++ b/man/rank_distance.Rd @@ -34,7 +34,7 @@ translation of \code{Rankcluster::distCayley} \insertCite{Grimonprez2016}{BayesM \examples{ # Distance between two vectors of rankings: -rank_distance(1:5,5:1, metric = "kendall") +rank_distance(1:5, 5:1, metric = "kendall") rank_distance(c(2, 4, 3, 6, 1, 7, 5), c(3, 5, 4, 7, 6, 2, 1), metric = "cayley") rank_distance(c(4, 2, 3, 1), c(3, 4, 1, 2), metric = "hamming") rank_distance(c(1, 3, 5, 7, 9, 8, 6, 4, 2), c(1, 2, 3, 4, 9, 8, 7, 6, 5), "ulam") diff --git a/man/sample_mallows.Rd b/man/sample_mallows.Rd index 41046c37..939f70aa 100644 --- a/man/sample_mallows.Rd +++ b/man/sample_mallows.Rd @@ -106,13 +106,12 @@ compute_posterior_intervals(model_fit, burnin = 2000, parameter = "alpha") library(PerMallows) # Set the scale parameter of the PerMallows package corresponding to # alpha0 in BayesMallows -theta0 = alpha0 / n_items +theta0 <- alpha0 / n_items # Sample with PerMallows::rmm sample1 <- rmm(n = 100, sigma0 = rho0, theta = theta0, dist.name = "cayley") # Generate the same sample with sample_mallows sample2 <- sample_mallows(rho0 = rho0, alpha0 = alpha0, n_samples = 100, burnin = 1000, thinning = 1000, metric = "cayley") - } \references{ \insertAllCited{} diff --git a/tests/testthat/test-compute_mallows.R b/tests/testthat/test-compute_mallows.R index 1af50787..d58a4cbf 100644 --- a/tests/testthat/test-compute_mallows.R +++ b/tests/testthat/test-compute_mallows.R @@ -5,7 +5,7 @@ context("Testing compute_mallows") test_that("miscellaneous input validation", { namat <- potato_visual - namat[c(1,2, 3), c(7, 9)] <- NA_real_ + namat[c(1, 2, 3), c(7, 9)] <- NA_real_ expect_error(compute_mallows(rankings = namat, na_action = "fail")) expect_output(compute_mallows(rankings = namat, nmc = 2, na_action = "omit"), "Omitting 9 rows from rankings due to NA values") @@ -20,7 +20,7 @@ test_that("miscellaneous input validation", { expect_error(compute_mallows(rankings = potato_visual, nmc = -100)) }) -test_that("rho_init is properly validated",{ +test_that("rho_init is properly validated", { m <- potato_visual expect_error(compute_mallows(rankings = m, rho_init = 1:(ncol(m) - 1))) expect_error(compute_mallows(rankings = m, rho_init = c(potato_true_ranking[-1], 22))) @@ -31,7 +31,7 @@ test_that("rho_init is properly validated",{ } ) -test_that("compute_mallows discovers inconsistent rankings",{ +test_that("compute_mallows discovers inconsistent rankings", { expect_error(compute_mallows( rankings = matrix(c(1, 2, -3, 1, 2, 3), nrow = 2, byrow = TRUE) @@ -82,7 +82,7 @@ test_that("compute_mallows handles integer preferences", { expect_s3_class(m, "BayesMallows") }) -test_that("compute_mallows handles data with lots of missings",{ +test_that("compute_mallows handles data with lots of missings", { R_partial2 <- structure(c(NA, NA, NA, NA, NA, NA, 9, NA, NA, 7, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, @@ -105,7 +105,7 @@ test_that("compute_mallows handles data with lots of missings",{ } ) -test_that("compute_mallows treats obs_freq properly",{ +test_that("compute_mallows treats obs_freq properly", { m1 <- compute_mallows(rankings = potato_visual, obs_freq = rep(1, nrow(potato_visual)), seed = 2233) m2 <- compute_mallows(rankings = potato_visual, seed = 2233) diff --git a/tests/testthat/test-distance_function.R b/tests/testthat/test-distance_function.R index 031f7097..3a5a756e 100644 --- a/tests/testthat/test-distance_function.R +++ b/tests/testthat/test-distance_function.R @@ -83,7 +83,7 @@ test_that("Ulam distance is correct", { test_that("Exported rank_distance is correct", { # Distance between two vectors of rankings: - expect_equal(rank_distance(1:5,5:1, metric = "kendall"), 10) + expect_equal(rank_distance(1:5, 5:1, metric = "kendall"), 10) expect_equal( rank_distance(c(2, 4, 3, 6, 1, 7, 5), c(3, 5, 4, 7, 6, 2, 1), metric = "cayley"), 6) diff --git a/tests/testthat/test-expected_dist.R b/tests/testthat/test-expected_dist.R index 564ea580..7d236fba 100644 --- a/tests/testthat/test-expected_dist.R +++ b/tests/testthat/test-expected_dist.R @@ -1,10 +1,10 @@ test_that("expected dist works", { - expect_equal(round(expected_dist(5,5,metric="kendall"), 6), 1.749137) - expect_equal(round(expected_dist(12,6,metric="cayley"), 6), 1.375779) - expect_equal(round(expected_dist(1.5 * 7,7,metric="hamming"), 6), 2.69246) - expect_equal(round(expected_dist(5 * 30,30,"ulam"), 6), 4.133538) - expect_equal(round(expected_dist(3.5 * 45,45,"footrule"), 6), 0.080459) - expect_equal(round(expected_dist(4 * 10,10,"spearman"), 6), 0.006033) + expect_equal(round(expected_dist(5, 5, metric = "kendall"), 6), 1.749137) + expect_equal(round(expected_dist(12, 6, metric = "cayley"), 6), 1.375779) + expect_equal(round(expected_dist(1.5 * 7, 7, metric = "hamming"), 6), 2.69246) + expect_equal(round(expected_dist(5 * 30, 30, "ulam"), 6), 4.133538) + expect_equal(round(expected_dist(3.5 * 45, 45, "footrule"), 6), 0.080459) + expect_equal(round(expected_dist(4 * 10, 10, "spearman"), 6), 0.006033) }) diff --git a/tests/testthat/test-generate_ranking.R b/tests/testthat/test-generate_ranking.R index 75007733..2d89f921 100644 --- a/tests/testthat/test-generate_ranking.R +++ b/tests/testthat/test-generate_ranking.R @@ -16,7 +16,7 @@ pair_comp <- tribble( pair_comp_tc <- generate_transitive_closure(pair_comp) beach_tc <- generate_transitive_closure(beach_preferences) -test_that("generate_initial_ranking works",{ +test_that("generate_initial_ranking works", { expect_error(generate_initial_ranking(pair_comp)) expect_is(generate_initial_ranking(pair_comp_tc), "matrix") @@ -25,7 +25,7 @@ test_that("generate_initial_ranking works",{ } ) -test_that("generate_initial_ranking with shuffle_unranked works",{ +test_that("generate_initial_ranking with shuffle_unranked works", { small_tc <- beach_tc[beach_tc$assessor %in% 1:6 & beach_tc$bottom_item %in% 1:4 & beach_tc$top_item %in% 1:4, ] set.seed(123) @@ -44,7 +44,7 @@ test_that("generate_initial_ranking with shuffle_unranked works",{ -test_that("generate_initial_ranking with random works",{ +test_that("generate_initial_ranking with random works", { small_tc <- beach_tc[beach_tc$assessor %in% 1:6 & beach_tc$bottom_item %in% 1:4 & beach_tc$top_item %in% 1:4, ] @@ -65,4 +65,3 @@ test_that("generate_initial_ranking with random works",{ expect_error(generate_initial_ranking(tc = beach_tc, random = TRUE)) } ) - diff --git a/tests/testthat/test-lik_db_mix.R b/tests/testthat/test-lik_db_mix.R index 0328f77d..c916f593 100644 --- a/tests/testthat/test-lik_db_mix.R +++ b/tests/testthat/test-lik_db_mix.R @@ -10,9 +10,9 @@ test_that("lik_db_mix works", { # Compute the likelihood and log-likelihood values under the true model... expect_equal( sprintf("%.3e", lik_db_mix( - rho = rbind(1:n_items,1:n_items), + rho = rbind(1:n_items, 1:n_items), alpha = c(2 * n_items, 2 * n_items), - weights = c(0.5,0.5), + weights = c(0.5, 0.5), metric = "kendall", rankings = mydata )), "1.434e-74") @@ -30,29 +30,29 @@ test_that("lik_db_mix works", { freq_distr <- rank_freq_distr(mydata) expect_equal( sprintf("%.3e", lik_db_mix( - rho = rbind(1:n_items,1:n_items), + rho = rbind(1:n_items, 1:n_items), alpha = c(2 * n_items, 2 * n_items), weights = c(0.5, 0.5), metric = "kendall", rankings = freq_distr[, 1:n_items], - obs_freq = freq_distr[,n_items+1] + obs_freq = freq_distr[, n_items + 1] )), "1.434e-74") expect_equal(round(lik_db_mix( rho = rbind(1:n_items, 1:n_items), alpha = c(2 * n_items, 2 * n_items), - weights=c(0.5, 0.5), + weights = c(0.5, 0.5), metric = "kendall", rankings = freq_distr[, 1:n_items], - obs_freq = freq_distr[, n_items+1], - log=TRUE + obs_freq = freq_distr[, n_items + 1], + log = TRUE ), 4), -170.0306) expect_error( lik_db_mix( - rho = rbind(1:n_items,1:n_items), + rho = rbind(1:n_items, 1:n_items), alpha = c(2 * n_items, 2 * n_items), - weights = c(0.5,0.5), + weights = c(0.5, 0.5), metric = "kendall", rankings = mydata, obs_freq = c(1, 2) diff --git a/tests/testthat/test-misc_functions.R b/tests/testthat/test-misc_functions.R index 512adaad..ed2d975e 100644 --- a/tests/testthat/test-misc_functions.R +++ b/tests/testthat/test-misc_functions.R @@ -1,7 +1,7 @@ context("Testing misc functions") test_that( - "validate_permutation is correct",{ + "validate_permutation is correct", { expect_equal( BayesMallows:::validate_permutation(c(1, 3, 3)), FALSE diff --git a/tests/testthat/test-plot.R b/tests/testthat/test-plot.R index bad97618..9791818b 100644 --- a/tests/testthat/test-plot.R +++ b/tests/testthat/test-plot.R @@ -22,6 +22,6 @@ test_that("plot.BayesMallows works", { expect_s3_class(plot(m, burnin = 4, parameter = "cluster_probs"), "ggplot") expect_s3_class(plot(m, burnin = 4, parameter = "cluster_assignment"), "ggplot") - m <- compute_mallows(preferences = beach_preferences[1:100,], nmc = 10, error_model = "bernoulli") + m <- compute_mallows(preferences = beach_preferences[1:100, ], nmc = 10, error_model = "bernoulli") expect_s3_class(plot(m, burnin = 3, parameter = "theta"), "ggplot") }) diff --git a/tests/testthat/test-smc_individual_functions.R b/tests/testthat/test-smc_individual_functions.R index ea63a153..baf937f0 100644 --- a/tests/testthat/test-smc_individual_functions.R +++ b/tests/testthat/test-smc_individual_functions.R @@ -1,6 +1,6 @@ context("SMC individual functions") -rho <- c(1,2,3,4,5,6) +rho <- c(1, 2, 3, 4, 5, 6) alpha <- 2 metric <- "footrule" n_items <- 6 @@ -21,7 +21,7 @@ test_that("get_mallows_loglik() works as expected", { alpha = alpha, rho = rho, n_items = n_items, rankings = rankings, metric = metric ) - expect_equivalent(loglik, -22.6667, tol=1e-4) + expect_equivalent(loglik, -22.6667, tol = 1e-4) }) test_that("smc_metropolis_hastings_rho() works as expected", { @@ -40,7 +40,7 @@ test_that("smc_metropolis_hastings_rho() works as expected", { alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, rho = rho, leap_size = 1 ) - dist_1 <- BayesMallows:::get_rank_distance(rho, test_1, metric= "ulam") + dist_1 <- BayesMallows:::get_rank_distance(rho, test_1, metric = "ulam") expect_equal(test_1, as.matrix(c(1, 2, 3, 5, 4, 6))) # if rho != rho_prime, then it should have a ulam distance of 1 # if rho == rho_prime, then it should have ulam distance of 0 @@ -83,31 +83,31 @@ test_that("smc_leap_and_shift_probs() works as expected", { # if leap_size = 1, then forwards_prob = backwards_prob test_1 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 1) expect_equal(test_1$rho_prime, as.matrix(c(1, 3, 2, 4, 5, 6))) - expect_equivalent(test_1$forwards_prob, 0.1666667, tol=1e-6) - expect_equivalent(test_1$backwards_prob, 0.1666667, tol=1e-6) + expect_equivalent(test_1$forwards_prob, 0.1666667, tol = 1e-6) + expect_equivalent(test_1$backwards_prob, 0.1666667, tol = 1e-6) # if rho != rho_prime, then it should have a ulam distance of 1 # if rho == rho_prime, then it should have ulam distance of 0 - dist_1 <- BayesMallows:::get_rank_distance(rho, test_1$rho_prime, metric= "ulam") + dist_1 <- BayesMallows:::get_rank_distance(rho, test_1$rho_prime, metric = "ulam") expect_equal(dist_1, 1) test_2 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 2) expect_equal(test_2$rho_prime, as.matrix(c(1, 2, 3, 4, 5, 6))) - expect_equivalent(test_2$forwards_prob, 0.0556, tol=1e-4) - expect_equivalent(test_2$backwards_prob, 0.0556, tol=1e-4) + expect_equivalent(test_2$forwards_prob, 0.0556, tol = 1e-4) + expect_equivalent(test_2$backwards_prob, 0.0556, tol = 1e-4) dist_2 <- get_rank_distance( - rho, test_2$rho_prime, metric= "ulam" + rho, test_2$rho_prime, metric = "ulam" ) expect_equal(dist_2, 0) test_3 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 3) expect_equal(test_3$rho_prime, as.matrix(c(1, 2, 3, 4, 5, 6))) - expect_equivalent(test_3$forwards_prob, 0.0417, tol=1e-3) - expect_equivalent(test_3$backwards_prob, 0.0417, tol=1e-3) + expect_equivalent(test_3$forwards_prob, 0.0417, tol = 1e-3) + expect_equivalent(test_3$backwards_prob, 0.0417, tol = 1e-3) dist_3 <- get_rank_distance( - rho, test_3$rho_prime, metric= "ulam" + rho, test_3$rho_prime, metric = "ulam" ) expect_equal(dist_3, 0) }) @@ -142,7 +142,7 @@ metropolis_hastings_alpha_old <- function( n_users <- length(rankings) / n_items loga <- n_users * (logz_alpha - logz_alpha_prime) + - dexp(alpha_prime, log=TRUE) - dexp(alpha, log=TRUE) + + dexp(alpha_prime, log = TRUE) - dexp(alpha, log = TRUE) + alpha_prime - alpha + mallows_loglik_prop # determine whether to accept or reject proposed rho and @@ -156,7 +156,7 @@ metropolis_hastings_alpha_old <- function( } set.seed(101) -rho <- c(1,2,3,4,5,6) +rho <- c(1, 2, 3, 4, 5, 6) alpha <- 2 metric <- "footrule" n_items <- 6 @@ -199,10 +199,10 @@ test_4_b <- metropolis_hastings_alpha( ) test_that("metropolis_hastings_alpha() works as expected", { - expect_equivalent(test_1_a, 1.951095, tol=1e-5) - expect_equivalent(test_1_b, 2.450351, tol=1e-5) - expect_equivalent(test_2_a, 1.951095, tol=1e-5) - expect_equivalent(test_2_b, 2.125639, tol=1e-5) + expect_equivalent(test_1_a, 1.951095, tol = 1e-5) + expect_equivalent(test_1_b, 2.450351, tol = 1e-5) + expect_equivalent(test_2_a, 1.951095, tol = 1e-5) + expect_equivalent(test_2_b, 2.125639, tol = 1e-5) expect_equivalent(test_3_b, 2) - expect_equivalent(test_4_b, 1.904542, tol=1e-5) + expect_equivalent(test_4_b, 1.904542, tol = 1e-5) }) diff --git a/tests/testthat/test-smc_mallows_complete_rankings.R b/tests/testthat/test-smc_mallows_complete_rankings.R index 1785f066..abe9c4a6 100755 --- a/tests/testthat/test-smc_mallows_complete_rankings.R +++ b/tests/testthat/test-smc_mallows_complete_rankings.R @@ -5,11 +5,11 @@ context("SMC complete rankings: sequence") ######################### set.seed(994) -data <- sushi_rankings[1:100,] +data <- sushi_rankings[1:100, ] # General n_items <- dim(sushi_rankings)[2] # Number of items -leap_size <- floor(n_items/5) +leap_size <- floor(n_items / 5) metric <- "footrule" # Generate estimate of Z_n(alpha) @@ -29,27 +29,27 @@ logz_estimate <- estimate_partition_function(method = "importance_sampling", ###################################### nmc <- 20 burnin <- 5 -model_fit <- compute_mallows(rankings = data, nmc = nmc, metric = metric, leap_size =leap_size, +model_fit <- compute_mallows(rankings = data, nmc = nmc, metric = metric, leap_size = leap_size, alpha_prop_sd = 0.15, logz_estimate = logz_estimate) model_fit$burnin <- burnin -alpha_samples_table <- data.frame(iteration = 1:nmc , value = model_fit$alpha$value) -alpha_samples_table <- alpha_samples_table[(burnin+1):nmc,] +alpha_samples_table <- data.frame(iteration = 1:nmc, value = model_fit$alpha$value) +alpha_samples_table <- alpha_samples_table[(burnin + 1):nmc, ] # from observing the plots, this looks like the estimated parameters of the Mallows Model -rho_0 <- c(4,5,2,6,8,3,9,1,7,10) +rho_0 <- c(4, 5, 2, 6, 8, 3, 9, 1, 7, 10) alpha_0 <- 1.7 # heatplot - there is no burnin! -mcmc_rho_matrix <- matrix(model_fit$rho$value, ncol = n_items, nrow = nmc, byrow=TRUE) +mcmc_rho_matrix <- matrix(model_fit$rho$value, ncol = n_items, nrow = nmc, byrow = TRUE) # ################################################################### # # SMC # ################################################################### mcmc_times <- 5 num_new_obs <- 10 -Time <- dim(data)[1]/num_new_obs +Time <- dim(data)[1] / num_new_obs N <- 100 test <- smc_mallows_new_users_complete( @@ -74,14 +74,14 @@ test_that("Output of smc_mallows_new_users_complete is OK", { # posterior confidence intervals for rho rho_temp <- compute_posterior_intervals_rho( - output = test$rho_samples[,,Time+1], nmc = N, burnin = 0 + output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0 ) # MAP AND CP consensus ranking estimates rho_cp <- compute_rho_consensus( - output = test$rho_samples[,,Time+1], nmc = N, burnin = 0, C = 1, type = "CP" + output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0, C = 1, type = "CP" ) -rho_map <- compute_rho_consensus(output = test$rho_samples[,,Time+1], nmc = N, burnin = 0, C = 1, type = "MAP") +rho_map <- compute_rho_consensus(output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0, C = 1, type = "MAP") test_that("Output of compute_posterior_intervals_rho is OK", { expect_is(rho_temp, "tbl_df") @@ -98,11 +98,11 @@ test_that("Output of compute_posterior_intervals_rho is OK", { # posterior for alpha alpha_samples_table <- data.frame( - iteration = 1:N , value = test$alpha_samples[,Time+1] + iteration = 1:N, value = test$alpha_samples[, Time + 1] ) # posterior confidence intervals alpha_posterior_intervals <- compute_posterior_intervals_alpha( - output = test$alpha_samples[,Time+1], nmc = N, burnin = 0 + output = test$alpha_samples[, Time + 1], nmc = N, burnin = 0 ) test_that("Output of compute_posterior_intervals_alpha is OK", { @@ -134,24 +134,24 @@ test_that("get_mallows_loglik() in smc_mallows_new_users_complete() works", { N <- 100 # rho_samples and alpha_samples -------------------------- # - rho_samples <- array(data=0, dim=c(N, n_items, (n_users + Time + 1))) + rho_samples <- array(data = 0, dim = c(N, n_items, (n_users + Time + 1))) for (ii in seq_len(N)){ - rho_samples[ii, , 1] <- sample(seq_len(n_items), n_items, replace=FALSE) + rho_samples[ii, , 1] <- sample(seq_len(n_items), n_items, replace = FALSE) } - alpha_samples <- matrix(nrow=N, ncol=(n_items + Time + 1)) - alpha_samples[, 1] <- rexp(N, rate=1) + alpha_samples <- matrix(nrow = N, ncol = (n_items + Time + 1)) + alpha_samples[, 1] <- rexp(N, rate = 1) # logz_estimate ------------------------------------------ # alpha_vector <- seq(from = 0, to = 15, by = 1) iter <- 3e2 degree <- 10 logz_estimate <- estimate_partition_function( - method="importance_sampling", alpha_vector=alpha_vector, - n_items=n_items, metric=metric, nmc=iter, degree=degree + method = "importance_sampling", alpha_vector = alpha_vector, + n_items = n_items, metric = metric, nmc = iter, degree = degree ) num_obs <- 0 - out_loglik <- vector(mode="numeric", length=Time) + out_loglik <- vector(mode = "numeric", length = Time) for (tt in seq_len(Time)) { num_obs <- num_obs + num_new_obs new_observed_rankings <- data[(num_obs - num_new_obs + 1):num_obs, ] diff --git a/tests/testthat/test-smc_mallows_partial_rankings.R b/tests/testthat/test-smc_mallows_partial_rankings.R index 18dec112..ce1f8dc4 100644 --- a/tests/testthat/test-smc_mallows_partial_rankings.R +++ b/tests/testthat/test-smc_mallows_partial_rankings.R @@ -58,10 +58,10 @@ test_that("BayesMallows MCMC Results are OK", { ) post_rho <- compute_posterior_intervals(bm_mcmc, parameter = "rho") post_alpha <- compute_posterior_intervals(bm_mcmc, parameter = "alpha") - expect_equal(dim(post_rho) , c(10, 7)) - expect_equal(dim(rho_cp) , c(10, 3)) - expect_equal(dim(rho_map) , c(10, 3)) - expect_equal(dim(post_alpha), c(1 , 6)) + expect_equal(dim(post_rho), c(10, 7)) + expect_equal(dim(rho_cp), c(10, 3)) + expect_equal(dim(rho_map), c(10, 3)) + expect_equal(dim(post_alpha), c(1, 6)) }) # SMC Analysis (alpha unknown) =========================== @@ -147,11 +147,11 @@ test_that("Runs with unif kernel", { expect_equal(dim(smc_unif$alpha_samples), c(N, 21)) expect_s3_class( - plot_alpha_posterior(smc_unif$alpha_samples[, Time+ 1], nmc = N, burnin = 2), + plot_alpha_posterior(smc_unif$alpha_samples[, Time + 1], nmc = N, burnin = 2), "ggplot") expect_s3_class( - plot_rho_posterior(smc_unif$rho_samples[, ,Time+ 1], nmc = N, burnin = 2, C = 1), + plot_rho_posterior(smc_unif$rho_samples[, , Time + 1], nmc = N, burnin = 2, C = 1), "ggplot") }) diff --git a/tests/testthat/test-smc_uniform.R b/tests/testthat/test-smc_uniform.R index a46176c4..f1012fcd 100644 --- a/tests/testthat/test-smc_uniform.R +++ b/tests/testthat/test-smc_uniform.R @@ -5,7 +5,7 @@ require("BayesMallows") # tests for M-H_aug_ranking function =========================================== -rho <- c(1,2,3,4,5,6) +rho <- c(1, 2, 3, 4, 5, 6) alpha <- 2 metric <- "footrule" n_items <- 6 diff --git a/tests/testthat/test-transitive_closure.R b/tests/testthat/test-transitive_closure.R index 37590901..a07ee05f 100644 --- a/tests/testthat/test-transitive_closure.R +++ b/tests/testthat/test-transitive_closure.R @@ -31,7 +31,7 @@ pair_comp_tc <- tribble( class(pair_comp_tc) <- c("BayesMallowsTC", class(pair_comp_tc)) -test_that("transitive closure generation works",{ +test_that("transitive closure generation works", { pair_comp_returned <- generate_transitive_closure(pair_comp) %>% arrange(assessor, bottom_item, top_item) @@ -41,10 +41,8 @@ test_that("transitive closure generation works",{ } ) -test_that("transitive closure generation discovers inconsistencies",{ +test_that("transitive closure generation discovers inconsistencies", { pair_comp_inc <- bind_rows(pair_comp, tibble(assessor = 1, bottom_item = 5L, top_item = 2L)) expect_error(invisible(capture.output(generate_transitive_closure(pair_comp_inc)))) }) - - From f0235a19672f766a00a66ce4b0e86ea273d10e0b Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 11:38:00 +0100 Subject: [PATCH 35/41] Replaced tab with space for indentation --- R/generate_transitive_closure.R | 1 - R/print.BayesMallows.R | 2 - R/print.BayesMallowsMixtures.R | 2 - .../metropolis_hastings_alpha_example.R | 22 +- .../smc_mallows_new_users_complete_example.R | 16 +- man/lik_db_mix.Rd | 8 +- man/metropolis_hastings_alpha.Rd | 22 +- man/rank_freq_distr.Rd | 4 +- man/smc_mallows_new_users_complete.Rd | 16 +- ...test-classes_compute_consensus_posterior.R | 72 ++--- tests/testthat/test-rank_freq_distr.R | 1 - .../testthat/test-smc_individual_functions.R | 302 +++++++++--------- .../test-smc_mallows_complete_rankings.R | 180 +++++------ .../testthat/test-smc_mallows_new_item_rank.R | 136 ++++---- tests/testthat/test-smc_uniform.R | 16 +- 15 files changed, 397 insertions(+), 403 deletions(-) diff --git a/R/generate_transitive_closure.R b/R/generate_transitive_closure.R index 66dfcd34..7457c782 100644 --- a/R/generate_transitive_closure.R +++ b/R/generate_transitive_closure.R @@ -104,4 +104,3 @@ generate_transitive_closure <- function(df, cl = NULL){ return(result) } - diff --git a/R/print.BayesMallows.R b/R/print.BayesMallows.R index 10cf1cc6..f29a1dce 100644 --- a/R/print.BayesMallows.R +++ b/R/print.BayesMallows.R @@ -22,5 +22,3 @@ print.BayesMallows <- function(x, ...){ cat("Bayesian Mallows Model with", x$n_items, "items and", x$n_assessors, "assessors.\n") cat("Use functions assess_convergence() or plot() to visualize the object.") } - - diff --git a/R/print.BayesMallowsMixtures.R b/R/print.BayesMallowsMixtures.R index ac874384..8c489c92 100644 --- a/R/print.BayesMallowsMixtures.R +++ b/R/print.BayesMallowsMixtures.R @@ -25,5 +25,3 @@ print.BayesMallowsMixtures <- function(x, ...){ cat("Collection of", length(x), "Bayesian Mallows Mixture Models with the following number of mixture components:\n", paste0(paste(n_clusters, collapse = ", "), ".")) } - - diff --git a/inst/examples/metropolis_hastings_alpha_example.R b/inst/examples/metropolis_hastings_alpha_example.R index 1d742cb5..3d182558 100644 --- a/inst/examples/metropolis_hastings_alpha_example.R +++ b/inst/examples/metropolis_hastings_alpha_example.R @@ -3,7 +3,7 @@ alpha <- 2 metric <- "footrule" n_items <- 6 rankings <- sample_mallows( - rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 + rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 ) alpha_vector <- seq(from = 0, to = 20, by = 0.1) iter <- 1e2 @@ -12,26 +12,26 @@ degree <- 10 # Estimate the logarithm of the partition function of the Mallows rank model # using the estimate partition function logz_estimate <- estimate_partition_function( - method = "importance_sampling", alpha_vector = alpha_vector, - n_items = n_items, metric = "footrule", nmc = iter, degree = degree + method = "importance_sampling", alpha_vector = alpha_vector, + n_items = n_items, metric = "footrule", nmc = iter, degree = degree ) metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd = 0.5, - lambda = 0.1, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd = 0.5, + lambda = 0.1, alpha_max = 20 ) metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, - alpha_prop_sd = 0.15, lambda = 0.1, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.1, alpha_max = 20 ) metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, - alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 20 ) metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, - alpha_prop_sd = 0.15, lambda = 0.15, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.15, alpha_max = 20 ) diff --git a/inst/examples/smc_mallows_new_users_complete_example.R b/inst/examples/smc_mallows_new_users_complete_example.R index 06e83a73..bf87d24e 100644 --- a/inst/examples/smc_mallows_new_users_complete_example.R +++ b/inst/examples/smc_mallows_new_users_complete_example.R @@ -4,18 +4,18 @@ n_items <- ncol(sushi_rankings) metric <- "footrule" num_new_obs <- 10 logz_estimate <- estimate_partition_function( - method = "importance_sampling", - alpha_vector = seq(from = 0, to = 15, by = 0.1), - n_items = n_items, metric = metric, nmc = 1e2, degree = 10 + method = "importance_sampling", + alpha_vector = seq(from = 0, to = 15, by = 0.1), + n_items = n_items, metric = metric, nmc = 1e2, degree = 10 ) # Calculating rho and alpha samples samples <- smc_mallows_new_users_complete( - R_obs = data, n_items = n_items, metric = metric, - leap_size = floor(n_items / 5), N = 100, Time = nrow(data) / num_new_obs, - mcmc_kernel_app = 5, logz_estimate = logz_estimate, - alpha_prop_sd = 0.1, lambda = 0.001, alpha_max = 1e6, - num_new_obs = num_new_obs, verbose = TRUE + R_obs = data, n_items = n_items, metric = metric, + leap_size = floor(n_items / 5), N = 100, Time = nrow(data) / num_new_obs, + mcmc_kernel_app = 5, logz_estimate = logz_estimate, + alpha_prop_sd = 0.1, lambda = 0.001, alpha_max = 1e6, + num_new_obs = num_new_obs, verbose = TRUE ) # Studying the structure of the output diff --git a/man/lik_db_mix.Rd b/man/lik_db_mix.Rd index f8ea229c..08e2c045 100644 --- a/man/lik_db_mix.Rd +++ b/man/lik_db_mix.Rd @@ -51,7 +51,7 @@ mydata <- sample_mallows( n_samples = 100, rho0 = 1:n_items, alpha0 = 10, - metric="kendall") + metric = "kendall") # Compute the likelihood and log-likelihood values under the true model... lik_db_mix( @@ -85,10 +85,10 @@ lik_db_mix( lik_db_mix( rho = rbind(1:n_items, 1:n_items), alpha = c(10, 10), - weights=c(0.5, 0.5), + weights = c(0.5, 0.5), metric = "kendall", rankings = freq_distr[, 1:n_items], - obs_freq = freq_distr[, n_items+1], - log=TRUE + obs_freq = freq_distr[, n_items + 1], + log = TRUE ) } diff --git a/man/metropolis_hastings_alpha.Rd b/man/metropolis_hastings_alpha.Rd index 72a9ce1a..5f4aba33 100644 --- a/man/metropolis_hastings_alpha.Rd +++ b/man/metropolis_hastings_alpha.Rd @@ -65,7 +65,7 @@ alpha <- 2 metric <- "footrule" n_items <- 6 rankings <- sample_mallows( - rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 + rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 ) alpha_vector <- seq(from = 0, to = 20, by = 0.1) iter <- 1e2 @@ -74,28 +74,28 @@ degree <- 10 # Estimate the logarithm of the partition function of the Mallows rank model # using the estimate partition function logz_estimate <- estimate_partition_function( - method = "importance_sampling", alpha_vector = alpha_vector, - n_items = n_items, metric = "footrule", nmc = iter, degree = degree + method = "importance_sampling", alpha_vector = alpha_vector, + n_items = n_items, metric = "footrule", nmc = iter, degree = degree ) metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd = 0.5, - lambda = 0.1, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd = 0.5, + lambda = 0.1, alpha_max = 20 ) metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, - alpha_prop_sd = 0.15, lambda = 0.1, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.1, alpha_max = 20 ) metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, - alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 20 ) metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, - alpha_prop_sd = 0.15, lambda = 0.15, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.15, alpha_max = 20 ) } \author{ diff --git a/man/rank_freq_distr.Rd b/man/rank_freq_distr.Rd index d0cc110c..5d8ea026 100644 --- a/man/rank_freq_distr.Rd +++ b/man/rank_freq_distr.Rd @@ -23,8 +23,8 @@ Construct the frequency distribution of the distinct ranking \examples{ # Create example data. We set the burn-in and thinning very low # for the sampling to go fast -data0 <- sample_mallows(rho0 = 1:5, alpha=10, n_samples = 1000, +data0 <- sample_mallows(rho0 = 1:5, alpha = 10, n_samples = 1000, burnin = 10, thinning = 1) # Find the frequency distribution -rank_freq_distr(rankings=data0) +rank_freq_distr(rankings = data0) } diff --git a/man/smc_mallows_new_users_complete.Rd b/man/smc_mallows_new_users_complete.Rd index 95bc025a..be298133 100644 --- a/man/smc_mallows_new_users_complete.Rd +++ b/man/smc_mallows_new_users_complete.Rd @@ -77,18 +77,18 @@ n_items <- ncol(sushi_rankings) metric <- "footrule" num_new_obs <- 10 logz_estimate <- estimate_partition_function( - method = "importance_sampling", - alpha_vector = seq(from = 0, to = 15, by = 0.1), - n_items = n_items, metric = metric, nmc = 1e2, degree = 10 + method = "importance_sampling", + alpha_vector = seq(from = 0, to = 15, by = 0.1), + n_items = n_items, metric = metric, nmc = 1e2, degree = 10 ) # Calculating rho and alpha samples samples <- smc_mallows_new_users_complete( - R_obs = data, n_items = n_items, metric = metric, - leap_size = floor(n_items / 5), N = 100, Time = nrow(data) / num_new_obs, - mcmc_kernel_app = 5, logz_estimate = logz_estimate, - alpha_prop_sd = 0.1, lambda = 0.001, alpha_max = 1e6, - num_new_obs = num_new_obs, verbose = TRUE + R_obs = data, n_items = n_items, metric = metric, + leap_size = floor(n_items / 5), N = 100, Time = nrow(data) / num_new_obs, + mcmc_kernel_app = 5, logz_estimate = logz_estimate, + alpha_prop_sd = 0.1, lambda = 0.001, alpha_max = 1e6, + num_new_obs = num_new_obs, verbose = TRUE ) # Studying the structure of the output diff --git a/tests/testthat/test-classes_compute_consensus_posterior.R b/tests/testthat/test-classes_compute_consensus_posterior.R index 7d826c18..79035395 100644 --- a/tests/testthat/test-classes_compute_consensus_posterior.R +++ b/tests/testthat/test-classes_compute_consensus_posterior.R @@ -19,26 +19,26 @@ alpha_vector <- seq(from = 0, to = 15, by = 0.1) iter <- 1e3 degree <- 10 logz_estimate <- estimate_partition_function( - method = "importance_sampling", alpha_vector = alpha_vector, - n_items = n_items, metric = metric, nmc = iter, degree = degree + method = "importance_sampling", alpha_vector = alpha_vector, + n_items = n_items, metric = metric, nmc = iter, degree = degree ) data <- sushi_rankings[1:100, ] leap_size <- floor(n_items / 5) nmc <- N <- 1000 Time <- 20 fit_smc <- smc_mallows_new_users_complete( - R_obs = data, n_items = n_items, metric = metric, leap_size = leap_size, - N = N, Time = Time, logz_estimate = logz_estimate, mcmc_kernel_app = 5, - num_new_obs = 5, alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 1e6 + R_obs = data, n_items = n_items, metric = metric, leap_size = leap_size, + N = N, Time = Time, logz_estimate = logz_estimate, mcmc_kernel_app = 5, + num_new_obs = 5, alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 1e6 ) fit_smc_alpha <- fit_smc$alpha_samples[, Time + 1] fit_smc_post_alpha <- compute_posterior_intervals_alpha( - output = fit_smc_alpha, nmc = nmc, burnin = 0, verbose = FALSE + output = fit_smc_alpha, nmc = nmc, burnin = 0, verbose = FALSE ) fit_smc_rho <- fit_smc$rho_samples[, , Time + 1] fit_smc_post_rho <- compute_posterior_intervals_rho( - output = fit_smc_rho, nmc = nmc, burnin = 0, - verbose = FALSE + output = fit_smc_rho, nmc = nmc, burnin = 0, + verbose = FALSE ) # Emulating the internal workings of compute_posterior_intervals =============== @@ -48,19 +48,19 @@ fit_smc_post_rho <- compute_posterior_intervals_rho( fit_bm_alpha <- fit_bm$alpha fit_bm_alpha <- dplyr::group_by(fit_bm_alpha, .data$cluster) class(fit_bm_alpha) <- c( - "posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame" + "posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame" ) fit_bm_post_internal_alpha <- .compute_posterior_intervals( - fit_bm_alpha, "alpha", .95, 3L + fit_bm_alpha, "alpha", .95, 3L ) fit_bm_rho <- fit_bm$rho fit_bm_rho <- dplyr::group_by(fit_bm_rho, .data$cluster) class(fit_bm_rho) <- c( - "posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame" + "posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame" ) fit_bm_post_internal_rho <- .compute_posterior_intervals( - fit_bm_rho, "rho", .95, 3L + fit_bm_rho, "rho", .95, 3L ) # SMC-Mallows -------------------------------------------- # @@ -70,10 +70,10 @@ fit_smc_alpha$n_clusters <- 1 fit_smc_alpha$cluster <- "Cluster 1" fit_smc_alpha <- dplyr::group_by(fit_smc_alpha, .data$cluster) class(fit_smc_alpha) <- c( - "posterior_SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame" + "posterior_SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame" ) fit_smc_post_internal_alpha <- .compute_posterior_intervals( - fit_smc_alpha, "alpha", .95, 3L + fit_smc_alpha, "alpha", .95, 3L ) fit_smc_rho <- smc_processing(fit_smc_rho) @@ -81,29 +81,29 @@ fit_smc_rho$n_clusters <- 1 fit_smc_rho$cluster <- "Cluster 1" fit_smc_rho <- dplyr::group_by(fit_smc_rho, .data$cluster) class(fit_smc_rho) <- c( - "posterior_SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame" + "posterior_SMCMallows", "grouped_df", "tbl_df", "tbl", "data.frame" ) fit_smc_post_internal_rho <- .compute_posterior_intervals( - fit_smc_alpha, "rho", .95, 3L, discrete = TRUE + fit_smc_alpha, "rho", .95, 3L, discrete = TRUE ) # Testing classes ============================================================== test_that("Classes are correctly attributed", { - expect_s3_class(fit_bm, "BayesMallows") - expect_s3_class(fit_smc, "SMCMallows") - expect_s3_class(fit_bm_post_alpha, "data.frame") - expect_s3_class(fit_bm_post_rho, "data.frame") - expect_s3_class(fit_smc_post_alpha, "data.frame") - expect_s3_class(fit_smc_post_rho, "data.frame") - expect_error(.compute_posterior_intervals(fit_bm_post_alpha)) - expect_error(.compute_posterior_intervals(fit_bm_post_rho)) - expect_error(.compute_posterior_intervals(fit_smc_post_alpha)) - expect_error(.compute_posterior_intervals(fit_smc_post_rho)) - expect_s3_class(fit_bm_post_internal_alpha, "data.frame") - expect_s3_class(fit_bm_post_internal_rho, "data.frame") - expect_s3_class(fit_smc_post_internal_alpha, "data.frame") - expect_s3_class(fit_smc_post_internal_rho, "data.frame") + expect_s3_class(fit_bm, "BayesMallows") + expect_s3_class(fit_smc, "SMCMallows") + expect_s3_class(fit_bm_post_alpha, "data.frame") + expect_s3_class(fit_bm_post_rho, "data.frame") + expect_s3_class(fit_smc_post_alpha, "data.frame") + expect_s3_class(fit_smc_post_rho, "data.frame") + expect_error(.compute_posterior_intervals(fit_bm_post_alpha)) + expect_error(.compute_posterior_intervals(fit_bm_post_rho)) + expect_error(.compute_posterior_intervals(fit_smc_post_alpha)) + expect_error(.compute_posterior_intervals(fit_smc_post_rho)) + expect_s3_class(fit_bm_post_internal_alpha, "data.frame") + expect_s3_class(fit_bm_post_internal_rho, "data.frame") + expect_s3_class(fit_smc_post_internal_alpha, "data.frame") + expect_s3_class(fit_smc_post_internal_rho, "data.frame") }) context("compute_consensus() classes") @@ -112,15 +112,15 @@ fit_bm_consensus_cp <- compute_consensus(fit_bm, type = "CP") fit_bm_consensus_map <- compute_consensus(fit_bm, type = "MAP") fit_smc_rho <- fit_smc$rho_samples[, , Time + 1] fit_smc_consensus_cp <- compute_rho_consensus( - output = fit_smc_rho, nmc = nmc, burnin = 0, C = 1, type = "CP" + output = fit_smc_rho, nmc = nmc, burnin = 0, C = 1, type = "CP" ) fit_smc_consensus_map <- compute_rho_consensus( - output = fit_smc_rho, nmc = nmc, burnin = 0, C = 1, type = "MAP" + output = fit_smc_rho, nmc = nmc, burnin = 0, C = 1, type = "MAP" ) test_that("Classes are correctly attributed", { - expect_s3_class(fit_bm_consensus_cp, "data.frame") - expect_s3_class(fit_bm_consensus_map, "data.frame") - expect_s3_class(fit_smc_consensus_cp, "data.frame") - expect_s3_class(fit_smc_consensus_map, "data.frame") + expect_s3_class(fit_bm_consensus_cp, "data.frame") + expect_s3_class(fit_bm_consensus_map, "data.frame") + expect_s3_class(fit_smc_consensus_cp, "data.frame") + expect_s3_class(fit_smc_consensus_map, "data.frame") }) diff --git a/tests/testthat/test-rank_freq_distr.R b/tests/testthat/test-rank_freq_distr.R index 53e81fda..e02fa213 100644 --- a/tests/testthat/test-rank_freq_distr.R +++ b/tests/testthat/test-rank_freq_distr.R @@ -61,4 +61,3 @@ test_that("rank_freq_distr works", { "", "", "", "", "", "", "", "freq"))) ) }) - diff --git a/tests/testthat/test-smc_individual_functions.R b/tests/testthat/test-smc_individual_functions.R index baf937f0..62e853dc 100644 --- a/tests/testthat/test-smc_individual_functions.R +++ b/tests/testthat/test-smc_individual_functions.R @@ -6,110 +6,110 @@ metric <- "footrule" n_items <- 6 test_that("get_mallows_loglik() works as expected", { - set.seed(101) - loglik <- get_mallows_loglik( - alpha = alpha, rho = t(rho), n_items = length(rho), rankings = t(rho), - metric = metric - ) - expect_equal(loglik, 0) - - rankings <- sample_mallows( - rho0 = rho, alpha0 = alpha, n_samples = 10, - burnin = 1000, thinning = 500 - ) - loglik <- get_mallows_loglik( - alpha = alpha, rho = rho, n_items = n_items, rankings = rankings, - metric = metric - ) - expect_equivalent(loglik, -22.6667, tol = 1e-4) + set.seed(101) + loglik <- get_mallows_loglik( + alpha = alpha, rho = t(rho), n_items = length(rho), rankings = t(rho), + metric = metric + ) + expect_equal(loglik, 0) + + rankings <- sample_mallows( + rho0 = rho, alpha0 = alpha, n_samples = 10, + burnin = 1000, thinning = 500 + ) + loglik <- get_mallows_loglik( + alpha = alpha, rho = rho, n_items = n_items, rankings = rankings, + metric = metric + ) + expect_equivalent(loglik, -22.6667, tol = 1e-4) }) test_that("smc_metropolis_hastings_rho() works as expected", { - set.seed(101) - # This functions uses get_mallows_log_lik and leap_and_shift_probs - # so if the checks match in those worker functions then it is very likely - # that this function will return the correct outputs. - rankings <- sample_mallows( - rho0 = rho, alpha0 = alpha, n_samples = 10, - burnin = 1000, thinning = 500 - ) - - # you can confirm the print statements inside the metropolis_hastings_rho - # match get_mallows_loglik and leap_and_shift_probs - test_1 <- metropolis_hastings_rho( - alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, - rho = rho, leap_size = 1 - ) - dist_1 <- BayesMallows:::get_rank_distance(rho, test_1, metric = "ulam") - expect_equal(test_1, as.matrix(c(1, 2, 3, 5, 4, 6))) - # if rho != rho_prime, then it should have a ulam distance of 1 - # if rho == rho_prime, then it should have ulam distance of 0 - expect_equal(dist_1, 1) - - test_2 <- metropolis_hastings_rho( - alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, - rho = rho, leap_size = 2 - ) - dist_2 <- BayesMallows:::get_rank_distance(rho, test_2, metric = "ulam") - expect_equal(test_2, as.matrix(c(1, 2, 3, 4, 5, 6))) - expect_equal(dist_2, 0) - - test_3 <- metropolis_hastings_rho( - alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, - rho = rho, leap_size = 3 - ) - dist_3 <- BayesMallows:::get_rank_distance(rho, test_3, metric = "ulam") - expect_equal(test_3, as.matrix(c(1, 2, 3, 4, 5, 6))) - expect_equal(dist_3, 0) - - # we have a ranking data set containing 10 rankings over 6 items - test_4 <- metropolis_hastings_rho( - alpha = alpha, n_items = n_items, rankings = rankings, metric = metric, - rho = rho, leap_size = 1 - ) - dist_4 <- BayesMallows:::get_rank_distance(rho, test_4, metric = "ulam") - expect_equal(test_4, as.matrix(c(1, 2, 3, 4, 5, 6))) - expect_equal(dist_4, 0) + set.seed(101) + # This functions uses get_mallows_log_lik and leap_and_shift_probs + # so if the checks match in those worker functions then it is very likely + # that this function will return the correct outputs. + rankings <- sample_mallows( + rho0 = rho, alpha0 = alpha, n_samples = 10, + burnin = 1000, thinning = 500 + ) + + # you can confirm the print statements inside the metropolis_hastings_rho + # match get_mallows_loglik and leap_and_shift_probs + test_1 <- metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, + rho = rho, leap_size = 1 + ) + dist_1 <- BayesMallows:::get_rank_distance(rho, test_1, metric = "ulam") + expect_equal(test_1, as.matrix(c(1, 2, 3, 5, 4, 6))) + # if rho != rho_prime, then it should have a ulam distance of 1 + # if rho == rho_prime, then it should have ulam distance of 0 + expect_equal(dist_1, 1) + + test_2 <- metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, + rho = rho, leap_size = 2 + ) + dist_2 <- BayesMallows:::get_rank_distance(rho, test_2, metric = "ulam") + expect_equal(test_2, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(dist_2, 0) + + test_3 <- metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = t(rho), metric = metric, + rho = rho, leap_size = 3 + ) + dist_3 <- BayesMallows:::get_rank_distance(rho, test_3, metric = "ulam") + expect_equal(test_3, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(dist_3, 0) + + # we have a ranking data set containing 10 rankings over 6 items + test_4 <- metropolis_hastings_rho( + alpha = alpha, n_items = n_items, rankings = rankings, metric = metric, + rho = rho, leap_size = 1 + ) + dist_4 <- BayesMallows:::get_rank_distance(rho, test_4, metric = "ulam") + expect_equal(test_4, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equal(dist_4, 0) }) test_that("smc_leap_and_shift_probs() works as expected", { - set.seed(101) - n_items <- length(rho) - - # leap_size has a possible range, the BayesMallows papers suggest - # leap_size = floor(n_items/5) but the leap_size can be up to n_items/2. - # Note that leap_size must be integered valued. - - # if leap_size = 1, then forwards_prob = backwards_prob - test_1 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 1) - expect_equal(test_1$rho_prime, as.matrix(c(1, 3, 2, 4, 5, 6))) - expect_equivalent(test_1$forwards_prob, 0.1666667, tol = 1e-6) - expect_equivalent(test_1$backwards_prob, 0.1666667, tol = 1e-6) - - # if rho != rho_prime, then it should have a ulam distance of 1 - # if rho == rho_prime, then it should have ulam distance of 0 - dist_1 <- BayesMallows:::get_rank_distance(rho, test_1$rho_prime, metric = "ulam") - expect_equal(dist_1, 1) - - test_2 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 2) - expect_equal(test_2$rho_prime, as.matrix(c(1, 2, 3, 4, 5, 6))) - expect_equivalent(test_2$forwards_prob, 0.0556, tol = 1e-4) - expect_equivalent(test_2$backwards_prob, 0.0556, tol = 1e-4) - - dist_2 <- get_rank_distance( - rho, test_2$rho_prime, metric = "ulam" - ) - expect_equal(dist_2, 0) - - test_3 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 3) - expect_equal(test_3$rho_prime, as.matrix(c(1, 2, 3, 4, 5, 6))) - expect_equivalent(test_3$forwards_prob, 0.0417, tol = 1e-3) - expect_equivalent(test_3$backwards_prob, 0.0417, tol = 1e-3) - - dist_3 <- get_rank_distance( - rho, test_3$rho_prime, metric = "ulam" - ) - expect_equal(dist_3, 0) + set.seed(101) + n_items <- length(rho) + + # leap_size has a possible range, the BayesMallows papers suggest + # leap_size = floor(n_items/5) but the leap_size can be up to n_items/2. + # Note that leap_size must be integered valued. + + # if leap_size = 1, then forwards_prob = backwards_prob + test_1 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 1) + expect_equal(test_1$rho_prime, as.matrix(c(1, 3, 2, 4, 5, 6))) + expect_equivalent(test_1$forwards_prob, 0.1666667, tol = 1e-6) + expect_equivalent(test_1$backwards_prob, 0.1666667, tol = 1e-6) + + # if rho != rho_prime, then it should have a ulam distance of 1 + # if rho == rho_prime, then it should have ulam distance of 0 + dist_1 <- BayesMallows:::get_rank_distance(rho, test_1$rho_prime, metric = "ulam") + expect_equal(dist_1, 1) + + test_2 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 2) + expect_equal(test_2$rho_prime, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equivalent(test_2$forwards_prob, 0.0556, tol = 1e-4) + expect_equivalent(test_2$backwards_prob, 0.0556, tol = 1e-4) + + dist_2 <- get_rank_distance( + rho, test_2$rho_prime, metric = "ulam" + ) + expect_equal(dist_2, 0) + + test_3 <- leap_and_shift_probs(rho = rho, n_items = n_items, leap_size = 3) + expect_equal(test_3$rho_prime, as.matrix(c(1, 2, 3, 4, 5, 6))) + expect_equivalent(test_3$forwards_prob, 0.0417, tol = 1e-3) + expect_equivalent(test_3$backwards_prob, 0.0417, tol = 1e-3) + + dist_3 <- get_rank_distance( + rho, test_3$rho_prime, metric = "ulam" + ) + expect_equal(dist_3, 0) }) # ======================================================== # @@ -117,42 +117,42 @@ test_that("smc_leap_and_shift_probs() works as expected", { # ======================================================== # metropolis_hastings_alpha_old <- function( - alpha, n_items, rankings, metric, rho, logz_estimate + alpha, n_items, rankings, metric, rho, logz_estimate ) { - exp_alpha_prime <- rlnorm(1, mean = alpha, sd = 0.15) # 1 - alpha_prime <- log(exp_alpha_prime) - - # evaluate the log-likelihood with current rankings - mallows_loglik_prop <- get_mallows_loglik( - alpha = (alpha_prime - alpha), rho = rho, n = n_items, - rankings = rankings, metric = metric - ) - - # evaluate the log estimate of the partition function - # for a particular value of alpha - logz_alpha <- get_partition_function( - n_items = n_items, alpha = alpha, logz_estimate = logz_estimate, - metric = metric - ) - logz_alpha_prime <- get_partition_function( - n_items = n_items, alpha = alpha_prime, logz_estimate = logz_estimate, - metric = metric - ) - - n_users <- length(rankings) / n_items - - loga <- n_users * (logz_alpha - logz_alpha_prime) + - dexp(alpha_prime, log = TRUE) - dexp(alpha, log = TRUE) + - alpha_prime - alpha + mallows_loglik_prop - - # determine whether to accept or reject proposed rho and - # return now consensus ranking - p <- runif(1, min = 0, max = 1) - if (log(p) <= loga) { - return(alpha_prime) - } else { - return(alpha) - } + exp_alpha_prime <- rlnorm(1, mean = alpha, sd = 0.15) # 1 + alpha_prime <- log(exp_alpha_prime) + + # evaluate the log-likelihood with current rankings + mallows_loglik_prop <- get_mallows_loglik( + alpha = (alpha_prime - alpha), rho = rho, n = n_items, + rankings = rankings, metric = metric + ) + + # evaluate the log estimate of the partition function + # for a particular value of alpha + logz_alpha <- get_partition_function( + n_items = n_items, alpha = alpha, logz_estimate = logz_estimate, + metric = metric + ) + logz_alpha_prime <- get_partition_function( + n_items = n_items, alpha = alpha_prime, logz_estimate = logz_estimate, + metric = metric + ) + + n_users <- length(rankings) / n_items + + loga <- n_users * (logz_alpha - logz_alpha_prime) + + dexp(alpha_prime, log = TRUE) - dexp(alpha, log = TRUE) + + alpha_prime - alpha + mallows_loglik_prop + + # determine whether to accept or reject proposed rho and + # return now consensus ranking + p <- runif(1, min = 0, max = 1) + if (log(p) <= loga) { + return(alpha_prime) + } else { + return(alpha) + } } set.seed(101) @@ -161,7 +161,7 @@ alpha <- 2 metric <- "footrule" n_items <- 6 rankings <- sample_mallows( - rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 + rho0 = rho, alpha0 = alpha, n_samples = 10, burnin = 1000, thinning = 500 ) alpha_vector <- seq(from = 0, to = 20, by = 1) iter <- 1e4 @@ -170,39 +170,39 @@ degree <- 10 # Estimate the logarithm of the partition function of the Mallows rank model # using the estimate partition function logz_estimate <- estimate_partition_function( - method = "importance_sampling", alpha_vector = alpha_vector, - n_items = n_items, metric = "footrule", nmc = iter, degree = degree + method = "importance_sampling", alpha_vector = alpha_vector, + n_items = n_items, metric = "footrule", nmc = iter, degree = degree ) set.seed(101) test_1_a <- metropolis_hastings_alpha_old(alpha, n_items, rankings, metric, rho, logz_estimate) test_1_b <- metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd = 0.5, - lambda = 0.1, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, alpha_prop_sd = 0.5, + lambda = 0.1, alpha_max = 20 ) set.seed(101) test_2_a <- metropolis_hastings_alpha_old( - alpha, n_items, rankings, metric, rho, logz_estimate + alpha, n_items, rankings, metric, rho, logz_estimate ) test_2_b <- metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, - alpha_prop_sd = 0.15, lambda = 0.1, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.1, alpha_max = 20 ) set.seed(101) test_3_b <- metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, - alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.5, lambda = 0.15, alpha_max = 20 ) set.seed(101) test_4_b <- metropolis_hastings_alpha( - alpha, n_items, rankings, metric, rho, logz_estimate, - alpha_prop_sd = 0.15, lambda = 0.15, alpha_max = 20 + alpha, n_items, rankings, metric, rho, logz_estimate, + alpha_prop_sd = 0.15, lambda = 0.15, alpha_max = 20 ) test_that("metropolis_hastings_alpha() works as expected", { - expect_equivalent(test_1_a, 1.951095, tol = 1e-5) - expect_equivalent(test_1_b, 2.450351, tol = 1e-5) - expect_equivalent(test_2_a, 1.951095, tol = 1e-5) - expect_equivalent(test_2_b, 2.125639, tol = 1e-5) - expect_equivalent(test_3_b, 2) - expect_equivalent(test_4_b, 1.904542, tol = 1e-5) + expect_equivalent(test_1_a, 1.951095, tol = 1e-5) + expect_equivalent(test_1_b, 2.450351, tol = 1e-5) + expect_equivalent(test_2_a, 1.951095, tol = 1e-5) + expect_equivalent(test_2_b, 2.125639, tol = 1e-5) + expect_equivalent(test_3_b, 2) + expect_equivalent(test_4_b, 1.904542, tol = 1e-5) }) diff --git a/tests/testthat/test-smc_mallows_complete_rankings.R b/tests/testthat/test-smc_mallows_complete_rankings.R index abe9c4a6..4cfb7127 100755 --- a/tests/testthat/test-smc_mallows_complete_rankings.R +++ b/tests/testthat/test-smc_mallows_complete_rankings.R @@ -53,19 +53,19 @@ Time <- dim(data)[1] / num_new_obs N <- 100 test <- smc_mallows_new_users_complete( - R_obs = data, n_items = n_items, metric = metric, - leap_size = leap_size, N = N, Time = Time, - logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_times, - alpha_prop_sd = 0.1, lambda = 0.001, alpha_max = 1e6, - num_new_obs = num_new_obs, verbose = FALSE + R_obs = data, n_items = n_items, metric = metric, + leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_times, + alpha_prop_sd = 0.1, lambda = 0.001, alpha_max = 1e6, + num_new_obs = num_new_obs, verbose = FALSE ) test_that("Output of smc_mallows_new_users_complete is OK", { - expect_s3_class(test, "SMCMallows") - expect_length(test, 2) - expect_named(test, c("rho_samples", "alpha_samples")) - expect_equal(dim(test$rho_samples), c(100, 10, 111)) - expect_equal(dim(test$alpha_samples), c(100, 111)) + expect_s3_class(test, "SMCMallows") + expect_length(test, 2) + expect_named(test, c("rho_samples", "alpha_samples")) + expect_equal(dim(test$rho_samples), c(100, 10, 111)) + expect_equal(dim(test$alpha_samples), c(100, 111)) }) # ############################### @@ -74,107 +74,107 @@ test_that("Output of smc_mallows_new_users_complete is OK", { # posterior confidence intervals for rho rho_temp <- compute_posterior_intervals_rho( - output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0 + output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0 ) # MAP AND CP consensus ranking estimates rho_cp <- compute_rho_consensus( - output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0, C = 1, type = "CP" + output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0, C = 1, type = "CP" ) rho_map <- compute_rho_consensus(output = test$rho_samples[, , Time + 1], nmc = N, burnin = 0, C = 1, type = "MAP") test_that("Output of compute_posterior_intervals_rho is OK", { - expect_is(rho_temp, "tbl_df") - expect_length(rho_temp, 7) - expect_named( - rho_temp, - c( - "item", "parameter", "mean", "median", "conf_level", "hpdi", - "central_interval" - ) - ) - expect_equivalent(sapply(rho_temp, length), rep(10, 7)) + expect_is(rho_temp, "tbl_df") + expect_length(rho_temp, 7) + expect_named( + rho_temp, + c( + "item", "parameter", "mean", "median", "conf_level", "hpdi", + "central_interval" + ) + ) + expect_equivalent(sapply(rho_temp, length), rep(10, 7)) }) # posterior for alpha alpha_samples_table <- data.frame( - iteration = 1:N, value = test$alpha_samples[, Time + 1] + iteration = 1:N, value = test$alpha_samples[, Time + 1] ) # posterior confidence intervals alpha_posterior_intervals <- compute_posterior_intervals_alpha( - output = test$alpha_samples[, Time + 1], nmc = N, burnin = 0 + output = test$alpha_samples[, Time + 1], nmc = N, burnin = 0 ) test_that("Output of compute_posterior_intervals_alpha is OK", { - expect_is(alpha_posterior_intervals, "tbl_df") - expect_length(alpha_posterior_intervals, 6) - expect_named( - alpha_posterior_intervals, - c( - "parameter", "mean", "median", "conf_level", "hpdi", - "central_interval" - ) - ) - expect_equivalent(sapply(alpha_posterior_intervals, length), rep(1, 6)) + expect_is(alpha_posterior_intervals, "tbl_df") + expect_length(alpha_posterior_intervals, 6) + expect_named( + alpha_posterior_intervals, + c( + "parameter", "mean", "median", "conf_level", "hpdi", + "central_interval" + ) + ) + expect_equivalent(sapply(alpha_posterior_intervals, length), rep(1, 6)) }) context("SMC complete rankings: breakdown") test_that("get_mallows_loglik() in smc_mallows_new_users_complete() works", { - # ======================================================== # - # Setup # - # ======================================================== # - - # Basic elements ----------------------------------------- # - data <- sushi_rankings[1:100, ] - n_users <- nrow(data) - n_items <- ncol(sushi_rankings) - Time <- nrow(data) / num_new_obs - num_new_obs <- 10 - N <- 100 - - # rho_samples and alpha_samples -------------------------- # - rho_samples <- array(data = 0, dim = c(N, n_items, (n_users + Time + 1))) - for (ii in seq_len(N)){ - rho_samples[ii, , 1] <- sample(seq_len(n_items), n_items, replace = FALSE) - } - alpha_samples <- matrix(nrow = N, ncol = (n_items + Time + 1)) - alpha_samples[, 1] <- rexp(N, rate = 1) - - # logz_estimate ------------------------------------------ # - alpha_vector <- seq(from = 0, to = 15, by = 1) - iter <- 3e2 - degree <- 10 - logz_estimate <- estimate_partition_function( - method = "importance_sampling", alpha_vector = alpha_vector, - n_items = n_items, metric = metric, nmc = iter, degree = degree - ) - - num_obs <- 0 - out_loglik <- vector(mode = "numeric", length = Time) - for (tt in seq_len(Time)) { - num_obs <- num_obs + num_new_obs - new_observed_rankings <- data[(num_obs - num_new_obs + 1):num_obs, ] - rho_samples[, , tt + 1] <- rho_samples[, , tt] - alpha_samples[, tt + 1] <- alpha_samples[, tt] - alpha_samples_ii <- alpha_samples[ii, tt + 1] - rho_samples_ii <- rho_samples[ii, , tt + 1] - for (ii in seq_len(N)) { - log_z_alpha <- BayesMallows:::get_partition_function( - n_items, alpha_samples_ii, NULL, logz_estimate, metric - ) - log_likelihood <- get_mallows_loglik( - alpha_samples_ii, t(rho_samples_ii), n_items, - new_observed_rankings, metric - ) - } - out_loglik[tt] <- log_likelihood - } - - # ======================================================== # - # Test # - # ======================================================== # - tolerance <- 0.1 - expect_gt(max(out_loglik), mean(out_loglik) * (1 + tolerance)) - expect_lt(min(out_loglik), mean(out_loglik) * (1 - tolerance)) + # ======================================================== # + # Setup # + # ======================================================== # + + # Basic elements ----------------------------------------- # + data <- sushi_rankings[1:100, ] + n_users <- nrow(data) + n_items <- ncol(sushi_rankings) + Time <- nrow(data) / num_new_obs + num_new_obs <- 10 + N <- 100 + + # rho_samples and alpha_samples -------------------------- # + rho_samples <- array(data = 0, dim = c(N, n_items, (n_users + Time + 1))) + for (ii in seq_len(N)) { + rho_samples[ii, , 1] <- sample(seq_len(n_items), n_items, replace = FALSE) + } + alpha_samples <- matrix(nrow = N, ncol = (n_items + Time + 1)) + alpha_samples[, 1] <- rexp(N, rate = 1) + + # logz_estimate ------------------------------------------ # + alpha_vector <- seq(from = 0, to = 15, by = 1) + iter <- 3e2 + degree <- 10 + logz_estimate <- estimate_partition_function( + method = "importance_sampling", alpha_vector = alpha_vector, + n_items = n_items, metric = metric, nmc = iter, degree = degree + ) + + num_obs <- 0 + out_loglik <- vector(mode = "numeric", length = Time) + for (tt in seq_len(Time)) { + num_obs <- num_obs + num_new_obs + new_observed_rankings <- data[(num_obs - num_new_obs + 1):num_obs, ] + rho_samples[, , tt + 1] <- rho_samples[, , tt] + alpha_samples[, tt + 1] <- alpha_samples[, tt] + alpha_samples_ii <- alpha_samples[ii, tt + 1] + rho_samples_ii <- rho_samples[ii, , tt + 1] + for (ii in seq_len(N)) { + log_z_alpha <- BayesMallows:::get_partition_function( + n_items, alpha_samples_ii, NULL, logz_estimate, metric + ) + log_likelihood <- get_mallows_loglik( + alpha_samples_ii, t(rho_samples_ii), n_items, + new_observed_rankings, metric + ) + } + out_loglik[tt] <- log_likelihood + } + + # ======================================================== # + # Test # + # ======================================================== # + tolerance <- 0.1 + expect_gt(max(out_loglik), mean(out_loglik) * (1 + tolerance)) + expect_lt(min(out_loglik), mean(out_loglik) * (1 - tolerance)) }) diff --git a/tests/testthat/test-smc_mallows_new_item_rank.R b/tests/testthat/test-smc_mallows_new_item_rank.R index c880b6e3..cee4603f 100644 --- a/tests/testthat/test-smc_mallows_new_item_rank.R +++ b/tests/testthat/test-smc_mallows_new_item_rank.R @@ -32,78 +32,78 @@ lambda <- 0.15 alpha_max <- 1e6 test_that("Produces the wrong metric and aug_method error", { - expect_error( - smc_mallows_new_item_rank_alpha_fixed( - alpha = alpha_0, n_items = n_items, R_obs = sample_dataset, - metric = "cayley", leap_size = leap_size, N = N, Time = Time, - logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, - alpha_prop_sd = alpha_prop_sd, lambda = lambda, - alpha_max = alpha_max, aug_method = "pseudolikelihood" - ) - ) - expect_error( - smc_mallows_new_item_rank( - n_items = n_items, R_obs = sample_dataset, - metric = "cayley", leap_size = leap_size, N = N, Time = Time, - logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, - alpha_prop_sd = alpha_prop_sd, lambda = lambda, - alpha_max = alpha_max, aug_method = "pseudolikelihood" - ) - ) + expect_error( + smc_mallows_new_item_rank_alpha_fixed( + alpha = alpha_0, n_items = n_items, R_obs = sample_dataset, + metric = "cayley", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "pseudolikelihood" + ) + ) + expect_error( + smc_mallows_new_item_rank( + n_items = n_items, R_obs = sample_dataset, + metric = "cayley", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "pseudolikelihood" + ) + ) }) test_that("Runs with unif kernel", { - smc_unif_alpha_fixed_unif <- suppressMessages( - smc_mallows_new_item_rank_alpha_fixed( - alpha = alpha_0, n_items = n_items, R_obs = sample_dataset, - metric = "footrule", leap_size = leap_size, N = N, Time = Time, - logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, - alpha_prop_sd = alpha_prop_sd, lambda = lambda, - alpha_max = alpha_max, aug_method = "random" - ) - ) - expect_is(smc_unif_alpha_fixed_unif, "list") - expect_length(smc_unif_alpha_fixed_unif, 1) - expect_equal(dim(smc_unif_alpha_fixed_unif$rho_samples), c(N, 6, 31)) - smc_unif <- suppressMessages( - smc_mallows_new_item_rank( - n_items = n_items, R_obs = sample_dataset, - metric = "footrule", leap_size = leap_size, N = N, Time = Time, - logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, - alpha_prop_sd = alpha_prop_sd, lambda = lambda, - alpha_max = alpha_max, aug_method = "random" - ) - ) - expect_is(smc_unif, "list") - expect_length(smc_unif, 2) - expect_equal(dim(smc_unif$rho_samples), c(N, 6, 31)) - expect_equal(dim(smc_unif$alpha_samples), c(N, 31)) + smc_unif_alpha_fixed_unif <- suppressMessages( + smc_mallows_new_item_rank_alpha_fixed( + alpha = alpha_0, n_items = n_items, R_obs = sample_dataset, + metric = "footrule", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "random" + ) + ) + expect_is(smc_unif_alpha_fixed_unif, "list") + expect_length(smc_unif_alpha_fixed_unif, 1) + expect_equal(dim(smc_unif_alpha_fixed_unif$rho_samples), c(N, 6, 31)) + smc_unif <- suppressMessages( + smc_mallows_new_item_rank( + n_items = n_items, R_obs = sample_dataset, + metric = "footrule", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "random" + ) + ) + expect_is(smc_unif, "list") + expect_length(smc_unif, 2) + expect_equal(dim(smc_unif$rho_samples), c(N, 6, 31)) + expect_equal(dim(smc_unif$alpha_samples), c(N, 31)) }) test_that("Runs with pseudo kernel", { - smc_unif_alpha_fixed_unif <- suppressMessages( - smc_mallows_new_item_rank_alpha_fixed( - alpha = alpha_0, n_items = n_items, R_obs = sample_dataset, - metric = "footrule", leap_size = leap_size, N = N, Time = Time, - logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, - alpha_prop_sd = alpha_prop_sd, lambda = lambda, - alpha_max = alpha_max, aug_method = "pseudolikelihood" - ) - ) - expect_is(smc_unif_alpha_fixed_unif, "list") - expect_length(smc_unif_alpha_fixed_unif, 1) - expect_equal(dim(smc_unif_alpha_fixed_unif$rho_samples), c(N, 6, 31)) - smc_unif <- suppressMessages( - smc_mallows_new_item_rank( - n_items = n_items, R_obs = sample_dataset, - metric = "footrule", leap_size = leap_size, N = N, Time = Time, - logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, - alpha_prop_sd = alpha_prop_sd, lambda = lambda, - alpha_max = alpha_max, aug_method = "pseudolikelihood" - ) - ) - expect_is(smc_unif, "list") - expect_length(smc_unif, 2) - expect_equal(dim(smc_unif$rho_samples), c(N, 6, 31)) - expect_equal(dim(smc_unif$alpha_samples), c(N, 31)) + smc_unif_alpha_fixed_unif <- suppressMessages( + smc_mallows_new_item_rank_alpha_fixed( + alpha = alpha_0, n_items = n_items, R_obs = sample_dataset, + metric = "footrule", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "pseudolikelihood" + ) + ) + expect_is(smc_unif_alpha_fixed_unif, "list") + expect_length(smc_unif_alpha_fixed_unif, 1) + expect_equal(dim(smc_unif_alpha_fixed_unif$rho_samples), c(N, 6, 31)) + smc_unif <- suppressMessages( + smc_mallows_new_item_rank( + n_items = n_items, R_obs = sample_dataset, + metric = "footrule", leap_size = leap_size, N = N, Time = Time, + logz_estimate = logz_estimate, mcmc_kernel_app = mcmc_kernel_app, + alpha_prop_sd = alpha_prop_sd, lambda = lambda, + alpha_max = alpha_max, aug_method = "pseudolikelihood" + ) + ) + expect_is(smc_unif, "list") + expect_length(smc_unif, 2) + expect_equal(dim(smc_unif$rho_samples), c(N, 6, 31)) + expect_equal(dim(smc_unif$alpha_samples), c(N, 31)) }) diff --git a/tests/testthat/test-smc_uniform.R b/tests/testthat/test-smc_uniform.R index f1012fcd..9680f854 100644 --- a/tests/testthat/test-smc_uniform.R +++ b/tests/testthat/test-smc_uniform.R @@ -18,11 +18,11 @@ test_that("MH-aug ranking works", { set.seed(584) test_1 <- metropolis_hastings_aug_ranking( current_ranking = R_curr, - partial_ranking = R_obs, - alpha = alpha, + partial_ranking = R_obs, + alpha = alpha, rho = rho, - n_items = n_items, - metric = metric + n_items = n_items, + metric = metric ) expect_equal(test_1, as.matrix(c(1, 2, 3, 6, 5, 4))) expect_equal(get_rank_distance(rho, test_1, metric = "ulam"), 2) @@ -42,7 +42,7 @@ test_that("MH-aug ranking works", { # One missing rank --------------------------------------- # R_curr <- c(1, 2, 3, 6, 5, 4) R_obs <- c(1, 2, 3, 6, 5, NA) - set.seed(545) + set.seed(545) test_3 <- metropolis_hastings_aug_ranking( current_ranking = R_curr, partial_ranking = R_obs, alpha = alpha, rho = rho, n_items = n_items, metric = metric @@ -59,7 +59,7 @@ test_that("correction_kernel works", { # Three missing ranks ------------------------------------ # R_curr <- c(1, 2, 3, 4, 5, 6) R_obs <- c(1, 2, 3, NA, NA, NA) - set.seed(879) + set.seed(879) test_4 <- correction_kernel(R_obs, R_curr, n_items) expect_equal(test_4$ranking, as.matrix(c(1, 2, 3, 6, 4, 5))) expect_equal(test_4$correction_prob, 1 / 6) @@ -68,7 +68,7 @@ test_that("correction_kernel works", { # Two missing ranks -------------------------------------- # R_curr <- c(1, 2, 3, 4, 5, 6) R_obs <- c(1, 2, 3, 5, NA, NA) - set.seed(706) + set.seed(706) test_5 <- correction_kernel(R_obs, R_curr, n_items) expect_equal(test_5$ranking, as.matrix(c(1, 2, 3, 5, 4, 6))) expect_equal(test_5$correction_prob, 0.5) @@ -77,7 +77,7 @@ test_that("correction_kernel works", { # No missing ranks --------------------------------------- # R_curr <- c(1, 2, 3, 4, 5, 6) R_obs <- c(1, 2, 3, 4, 5, 6) - set.seed(731) + set.seed(731) test_6 <- correction_kernel(R_obs, R_curr, n_items) expect_equal(test_6$ranking, as.matrix(c(1, 2, 3, 4, 5, 6))) expect_equal(test_6$correction_prob, 1) From f41d7ffd4ca2bb62e143e9676674dc3273f66301 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 11:53:04 +0100 Subject: [PATCH 36/41] Fixed spacing before curly brace --- R/all_topological_sorts.R | 6 +- R/assess_convergence.R | 56 +++++++++---------- R/assign_cluster.R | 12 ++-- R/compute_consensus.R | 46 +++++++-------- R/compute_mallows.R | 40 ++++++------- R/compute_mallows_mixtures.R | 6 +- R/compute_posterior_intervals.R | 10 ++-- R/estimate_partition_function.R | 12 ++-- R/expected_dist.R | 16 +++--- R/generate_constraints.R | 4 +- R/generate_initial_ranking.R | 18 +++--- R/generate_transitive_closure.R | 8 +-- R/lik_db_mix.R | 12 ++-- R/misc.R | 22 ++++---- R/misc_expected_dist.R | 12 ++-- R/misc_likelihood.R | 14 ++--- R/plot.BayesMallows.R | 20 +++---- R/plot_elbow.R | 8 +-- R/plot_top_k.R | 10 ++-- R/predict_top_k.R | 12 ++-- R/print.BayesMallows.R | 2 +- R/print.BayesMallowsMixtures.R | 2 +- R/rank_conversion.R | 10 ++-- R/rank_distance.R | 6 +- R/rank_freq_distr.R | 4 +- R/sample_mallows.R | 14 ++--- R/smc_post_processing_functions.R | 12 ++-- R/tidy_mcmc.R | 32 +++++------ tests/testthat/test-compute_mallows.R | 4 +- tests/testthat/test-distance_function.R | 14 ++--- .../test-estimate_partition_function.R | 2 +- tests/testthat/test-misc_cpp.R | 4 +- tests/testthat/test-partition_function.R | 28 +++++----- tests/testthat/test-rank_freq_distr.R | 2 +- tests/testthat/test-sample_mallows.R | 2 +- 35 files changed, 241 insertions(+), 241 deletions(-) diff --git a/R/all_topological_sorts.R b/R/all_topological_sorts.R index ec71695b..2b8ad533 100644 --- a/R/all_topological_sorts.R +++ b/R/all_topological_sorts.R @@ -1,10 +1,10 @@ # Translation to R of C++ and Python code found here # https://www.geeksforgeeks.org/all-topological-sorts-of-a-directed-acyclic-graph/ -all_topological_sorts <- function(graph, path, discovered, n_items){ +all_topological_sorts <- function(graph, path, discovered, n_items) { flag <- FALSE - for(i in seq_len(n_items)){ - if(attr(graph, "indegree")[[i]] == 0 && !discovered[[i]]){ + for(i in seq_len(n_items)) { + if(attr(graph, "indegree")[[i]] == 0 && !discovered[[i]]) { attr(graph, "indegree")[graph[[i]]] <- attr(graph, "indegree")[graph[[i]]] - 1 path <- c(path, i) diff --git a/R/assess_convergence.R b/R/assess_convergence.R index 62aac0be..16d81f57 100644 --- a/R/assess_convergence.R +++ b/R/assess_convergence.R @@ -26,17 +26,17 @@ #' @export #' assess_convergence <- function(model_fit, parameter = "alpha", items = NULL, - assessors = NULL){ + assessors = NULL) { stopifnot(inherits(model_fit, "BayesMallows") || inherits(model_fit, "BayesMallowsMixtures")) if(parameter == "alpha") { - if(inherits(model_fit, "BayesMallows")){ + if(inherits(model_fit, "BayesMallows")) { m <- model_fit$alpha trace_alpha(m, FALSE) - } else if(inherits(model_fit, "BayesMallowsMixtures")){ - m <- do.call(rbind, lapply(model_fit, function(x){ + } else if(inherits(model_fit, "BayesMallowsMixtures")) { + m <- do.call(rbind, lapply(model_fit, function(x) { dplyr::mutate(x$alpha, cluster = as.character(.data$cluster), n_clusters = x$n_clusters) @@ -44,25 +44,25 @@ assess_convergence <- function(model_fit, parameter = "alpha", items = NULL, trace_alpha(m, TRUE) } - } else if(parameter == "rho"){ - if(inherits(model_fit, "BayesMallows")){ + } else if(parameter == "rho") { + if(inherits(model_fit, "BayesMallows")) { trace_rho(model_fit, items) - } else if(inherits(model_fit, "BayesMallowsMixtures")){ + } else if(inherits(model_fit, "BayesMallowsMixtures")) { cowplot::plot_grid(plotlist = lapply(model_fit, trace_rho, clusters = TRUE, items = items)) } } else if(parameter == "Rtilde") { - if(inherits(model_fit, "BayesMallows")){ + if(inherits(model_fit, "BayesMallows")) { trace_rtilde(model_fit, items, assessors) - } else if(inherits(model_fit, "BayesMallowsMixtures")){ + } else if(inherits(model_fit, "BayesMallowsMixtures")) { stop("Trace plots of augmented data not supported for BayesMallowsMixtures. Please rerun each component k using the k-th list element.") } - } else if (parameter == "cluster_probs"){ - if(inherits(model_fit, "BayesMallows")){ + } else if (parameter == "cluster_probs") { + if(inherits(model_fit, "BayesMallows")) { m <- model_fit$cluster_probs - } else if(inherits(model_fit, "BayesMallowsMixtures")){ - m <- do.call(rbind, lapply(model_fit, function(x){ + } else if(inherits(model_fit, "BayesMallowsMixtures")) { + m <- do.call(rbind, lapply(model_fit, function(x) { dplyr::mutate(x$cluster_probs, cluster = as.character(.data$cluster), n_clusters = x$n_clusters) @@ -70,20 +70,20 @@ assess_convergence <- function(model_fit, parameter = "alpha", items = NULL, } trace_cluster_probs(m) - } else if (parameter == "theta"){ + } else if (parameter == "theta") { trace_theta(model_fit) } else { stop("parameter must be either \"alpha\", \"rho\", \"augmentation\", \"cluster_probs\", or \"theta\".") } } -trace_alpha <- function(m, clusters){ +trace_alpha <- function(m, clusters) { # Create the diagnostic plot for alpha p <- ggplot2::ggplot(m, ggplot2::aes(x = .data$iteration, y = .data$value)) + ggplot2::xlab("Iteration") + ggplot2::ylab(expression(alpha)) - if(!clusters){ + if(!clusters) { p <- p + ggplot2::geom_line() } else { p <- p + @@ -97,16 +97,16 @@ trace_alpha <- function(m, clusters){ return(p) } -trace_rho <- function(model_fit, items, clusters = model_fit$n_clusters > 1){ +trace_rho <- function(model_fit, items, clusters = model_fit$n_clusters > 1) { - if(is.null(items) && model_fit$n_items > 5){ + if(is.null(items) && model_fit$n_items > 5) { message("Items not provided by user. Picking 5 at random.") items <- sample.int(model_fit$n_items, 5) } else if (is.null(items) && model_fit$n_items > 0) { items <- seq.int(from = 1, to = model_fit$n_items) } - if(!is.character(items)){ + if(!is.character(items)) { items <- model_fit$items[items] } @@ -118,7 +118,7 @@ trace_rho <- function(model_fit, items, clusters = model_fit$n_clusters > 1){ ggplot2::xlab("Iteration") + ggplot2::ylab(expression(rho)) - if(clusters){ + if(clusters) { p <- p + ggplot2::facet_wrap(ggplot2::vars(.data$cluster)) } @@ -126,21 +126,21 @@ trace_rho <- function(model_fit, items, clusters = model_fit$n_clusters > 1){ } -trace_rtilde <- function(model_fit, items, assessors, ...){ +trace_rtilde <- function(model_fit, items, assessors, ...) { - if(!model_fit$save_aug){ + if(!model_fit$save_aug) { stop("Please rerun with compute_mallows with save_aug = TRUE") } - if(is.null(items) && model_fit$n_items > 5){ + if(is.null(items) && model_fit$n_items > 5) { message("Items not provided by user. Picking 5 at random.") items <- sample.int(model_fit$n_items, 5) } else if (is.null(items) && model_fit$n_items > 0) { items <- seq.int(from = 1, to = model_fit$n_items) } - if(is.null(assessors) && model_fit$n_assessors > 5){ + if(is.null(assessors) && model_fit$n_assessors > 5) { message("Assessors not provided by user. Picking 5 at random.") assessors <- sample.int(model_fit$n_assessors, 5) } else if (is.null(assessors) && model_fit$n_assessors > 0) { @@ -151,7 +151,7 @@ trace_rtilde <- function(model_fit, items, assessors, ...){ } } - if(is.factor(model_fit$augmented_data$item) && is.numeric(items)){ + if(is.factor(model_fit$augmented_data$item) && is.numeric(items)) { items <- levels(model_fit$augmented_data$item)[items] } df <- dplyr::filter(model_fit$augmented_data, @@ -169,7 +169,7 @@ trace_rtilde <- function(model_fit, items, assessors, ...){ } -trace_cluster_probs <- function(m){ +trace_cluster_probs <- function(m) { ggplot2::ggplot(m, ggplot2::aes(x = .data$iteration, y = .data$value, color = .data$cluster)) + @@ -184,8 +184,8 @@ trace_cluster_probs <- function(m){ } -trace_theta <- function(model_fit){ - if(is.null(model_fit$theta) || length(model_fit$theta) == 0){ +trace_theta <- function(model_fit) { + if(is.null(model_fit$theta) || length(model_fit$theta) == 0) { stop("Theta not available. Run compute_mallows with error_model = 'bernoulli'.") } # Create the diagnostic plot for theta diff --git a/R/assign_cluster.R b/R/assign_cluster.R index 4198041e..f1268448 100644 --- a/R/assign_cluster.R +++ b/R/assign_cluster.R @@ -30,12 +30,12 @@ #' #' @export #' -assign_cluster <- function(model_fit, burnin = model_fit$burnin, soft = TRUE, expand = FALSE){ +assign_cluster <- function(model_fit, burnin = model_fit$burnin, soft = TRUE, expand = FALSE) { - if(is.null(burnin)){ + if(is.null(burnin)) { stop("Please specify the burnin.") } - if(is.null(model_fit$cluster_assignment)){ + if(is.null(model_fit$cluster_assignment)) { stop("Rerun compute_mallows with save_clus=TRUE.") } stopifnot(burnin < model_fit$nmc) @@ -53,8 +53,8 @@ assign_cluster <- function(model_fit, burnin = model_fit$burnin, soft = TRUE, ex df <- dplyr::ungroup(df) df <- dplyr::rename(df, cluster = .data$value) - if(expand){ - df <- do.call(rbind, lapply(split(df, f = df$assessor), function(dd){ + if(expand) { + df <- do.call(rbind, lapply(split(df, f = df$assessor), function(dd) { dd2 <- merge(dd, expand.grid(cluster = unique(df$cluster)), by = "cluster", all = TRUE) dd2$assessor <- unique(dd$assessor) @@ -78,7 +78,7 @@ assign_cluster <- function(model_fit, burnin = model_fit$burnin, soft = TRUE, ex # Join map back onto df df <- dplyr::inner_join(df, map, by = "assessor") - if(!soft){ + if(!soft) { df <- dplyr::filter(df, .data$cluster == .data$map_cluster) df <- dplyr::select(df, -.data$cluster) } diff --git a/R/compute_consensus.R b/R/compute_consensus.R index f064cc31..bbf770c1 100644 --- a/R/compute_consensus.R +++ b/R/compute_consensus.R @@ -44,18 +44,18 @@ compute_consensus.BayesMallows <- function( model_fit, type = "CP", burnin = model_fit$burnin, parameter = "rho", assessors = 1L, ... ) { - if(is.null(burnin)){ + if(is.null(burnin)) { stop("Please specify the burnin.") } stopifnot(burnin < model_fit$nmc) stopifnot(class(model_fit) == "BayesMallows") - if(parameter == "Rtilde" && !inherits(model_fit$augmented_data, "data.frame")){ + if(parameter == "Rtilde" && !inherits(model_fit$augmented_data, "data.frame")) { stop("For augmented ranks, please refit model with option 'save_aug = TRUE'.") } - if(parameter == "rho"){ + if(parameter == "rho") { # Filter out the pre-burnin iterations df <- dplyr::filter(model_fit$rho, .data$iteration > burnin) @@ -71,14 +71,14 @@ compute_consensus.BayesMallows <- function( class(df) <- c("consensus_BayesMallows", "tbl_df", "tbl", "data.frame") - df <- if(type == "CP"){ + df <- if(type == "CP") { .compute_cp_consensus(df) - } else if(type == "MAP"){ + } else if(type == "MAP") { .compute_map_consensus(df) } - } else if(parameter == "Rtilde"){ + } else if(parameter == "Rtilde") { # Filter out the pre-burnin iterations and get the right assessors df <- dplyr::filter(model_fit$augmented_data, .data$iteration > burnin, .data$assessor %in% assessors) @@ -96,13 +96,13 @@ compute_consensus.BayesMallows <- function( df <- dplyr::rename(df, cluster = "assessor") class(df) <- c("consensus_BayesMallows", "tbl_df", "tbl", "data.frame") - df <- if(type == "CP"){ + df <- if(type == "CP") { .compute_cp_consensus(df) - } else if(type == "MAP"){ + } else if(type == "MAP") { .compute_map_consensus(df) } - if("cluster" %in% names(df)){ + if("cluster" %in% names(df)) { df <- dplyr::rename(df, assessor = "cluster") } @@ -136,7 +136,7 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { } } -.compute_cp_consensus.consensus_BayesMallows <- function(df){ +.compute_cp_consensus.consensus_BayesMallows <- function(df) { # Convert items and cluster to character, since factor levels are not needed in this case df <- dplyr::mutate_at(df, dplyr::vars(.data$item, .data$cluster), @@ -168,15 +168,15 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { # If there is only one cluster, we drop the cluster column - if(length(unique(df$cluster)) == 1){ + if(length(unique(df$cluster)) == 1) { df <- dplyr::select(df, -.data$cluster) } return(df) } -.compute_cp_consensus.consensus_SMCMallows <- function(model_fit, burnin){ - if(is.null(burnin)){ +.compute_cp_consensus.consensus_SMCMallows <- function(model_fit, burnin) { + if(is.null(burnin)) { stop("Please specify the burnin.") } @@ -184,7 +184,7 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { # Filter out the pre-burnin iterations - if(burnin != 0){ + if(burnin != 0) { df <- dplyr::filter(model_fit, .data$iteration > burnin) } else { df <- model_fit @@ -235,7 +235,7 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { } # Internal function for finding CP consensus. -find_cpc.consensus_BayesMallows <- function(group_df){ +find_cpc.consensus_BayesMallows <- function(group_df) { # Declare the result dataframe before adding rows to it result <- dplyr::tibble( cluster = character(), @@ -245,7 +245,7 @@ find_cpc.consensus_BayesMallows <- function(group_df){ ) n_items <- max(group_df$value) - for(i in seq(from = 1, to = n_items, by = 1)){ + for(i in seq(from = 1, to = n_items, by = 1)) { # Filter out the relevant rows tmp_df <- dplyr::filter(group_df, .data$value == i) @@ -268,7 +268,7 @@ find_cpc.consensus_BayesMallows <- function(group_df){ } # Internal function for finding CP consensus. -find_cpc.consensus_SMCMallows <- function(group_df){ +find_cpc.consensus_SMCMallows <- function(group_df) { # Declare the result dataframe before adding rows to it result <- dplyr::tibble( cluster = character(), @@ -277,7 +277,7 @@ find_cpc.consensus_SMCMallows <- function(group_df){ cumprob = numeric() ) n_items <- max(group_df$value) - for(i in seq(from = 1, to = n_items, by = 1)){ + for(i in seq(from = 1, to = n_items, by = 1)) { # Filter out the relevant rows tmp_df <- dplyr::filter(group_df, group_df$value == i) @@ -305,7 +305,7 @@ find_cpc.consensus_SMCMallows <- function(group_df){ return(result) } -.compute_map_consensus.consensus_BayesMallows <- function(df){ +.compute_map_consensus.consensus_BayesMallows <- function(df) { # Store the total number of iterations after burnin n_samples <- length(unique(df$iteration)) @@ -344,7 +344,7 @@ find_cpc.consensus_SMCMallows <- function(group_df){ # Sort according to cluster and ranking df <- dplyr::arrange(df, .data$cluster, .data$map_ranking) - if(length(unique(df$cluster)) == 1){ + if(length(unique(df$cluster)) == 1) { df <- dplyr::select(df, -.data$cluster) } @@ -355,12 +355,12 @@ find_cpc.consensus_SMCMallows <- function(group_df){ } #AS: added one extra line of code to resolve of the issues in #118 with plotting too many rows in compute_rho_consensus -.compute_map_consensus.consensus_SMCMallows <- function(model_fit, burnin = model_fit$burnin){ - if(is.null(burnin)){ +.compute_map_consensus.consensus_SMCMallows <- function(model_fit, burnin = model_fit$burnin) { + if(is.null(burnin)) { stop("Please specify the burnin.") } - if(burnin != 0){ + if(burnin != 0) { df <- dplyr::filter(model_fit, .data$iteration > burnin) } else { df <- model_fit diff --git a/R/compute_mallows.R b/R/compute_mallows.R index 01b7710b..da0756c3 100644 --- a/R/compute_mallows.R +++ b/R/compute_mallows.R @@ -237,17 +237,17 @@ compute_mallows <- function(rankings = NULL, constraints = NULL, save_ind_clus = FALSE, seed = NULL - ){ + ) { if(!is.null(seed)) set.seed(seed) # Check if there are NAs in rankings, if it is provided - if(!is.null(rankings)){ - if(na_action == "fail" && any(is.na(rankings))){ + if(!is.null(rankings)) { + if(na_action == "fail" && any(is.na(rankings))) { stop("rankings matrix contains NA values") } - if(na_action == "omit" && any(is.na(rankings))){ + if(na_action == "omit" && any(is.na(rankings))) { keeps <- apply(rankings, 1, function(x) !any(is.na(x))) print(paste("Omitting", sum(keeps), "rows from rankings due to NA values")) rankings <- rankings[keeps, , drop = FALSE] @@ -255,20 +255,20 @@ compute_mallows <- function(rankings = NULL, } # Check that at most one of rankings and preferences is set - if(is.null(rankings) && is.null(preferences)){ + if(is.null(rankings) && is.null(preferences)) { stop("Either rankings or preferences (or both) must be provided.") } - if(is.null(preferences) && !is.null(error_model)){ + if(is.null(preferences) && !is.null(error_model)) { stop("Error model requires preferences to be set.") } # Check if obs_freq are provided - if(!is.null(obs_freq)){ - if(is.null(rankings)){ + if(!is.null(obs_freq)) { + if(is.null(rankings)) { stop("rankings matrix must be provided when obs_freq are provided") } - if(nrow(rankings) != length(obs_freq)){ + if(nrow(rankings) != length(obs_freq)) { stop("obs_freq must be of same length as the number of rows in rankings") } } @@ -286,15 +286,15 @@ compute_mallows <- function(rankings = NULL, if(lambda <= 0) stop("exponential rate parameter lambda must be strictly positive") # Check that all rows of rankings are proper permutations - if(!is.null(rankings) && validate_rankings && !all(apply(rankings, 1, validate_permutation))){ + if(!is.null(rankings) && validate_rankings && !all(apply(rankings, 1, validate_permutation))) { stop("invalid permutations provided in rankings matrix") } # Deal with pairwise comparisons. Generate rankings compatible with them. - if(!is.null(preferences) && is.null(error_model)){ + if(!is.null(preferences) && is.null(error_model)) { - if(!inherits(preferences, "BayesMallowsTC")){ + if(!inherits(preferences, "BayesMallowsTC")) { message("Generating transitive closure of preferences.") # Make sure the preference columns are double preferences <- dplyr::mutate(preferences, @@ -304,15 +304,15 @@ compute_mallows <- function(rankings = NULL, preferences <- generate_transitive_closure(preferences) } - if(is.null(rankings)){ + if(is.null(rankings)) { message("Generating initial ranking.") rankings <- generate_initial_ranking(preferences) } - } else if(!is.null(error_model)){ + } else if(!is.null(error_model)) { stopifnot(error_model == "bernoulli") n_items <- max(c(preferences$bottom_item, preferences$top_item)) n_assessors <- length(unique(preferences$assessor)) - if(is.null(rankings)){ + if(is.null(rankings)) { rankings <- replicate(n_assessors, sample(x = n_items, size = n_items), simplify = "numeric") rankings <- matrix(rankings, ncol = n_items, nrow = n_assessors, byrow = TRUE) } @@ -322,7 +322,7 @@ compute_mallows <- function(rankings = NULL, n_items <- ncol(rankings) # If any row of rankings has only one missing value, replace it with the implied ranking - if(any(is.na(rankings))){ + if(any(is.na(rankings))) { dn <- dimnames(rankings) rankings <- lapply(split(rankings, f = seq_len(nrow(rankings))), @@ -342,9 +342,9 @@ compute_mallows <- function(rankings = NULL, } # Generate the constraint set - if(!is.null(preferences) && is.null(constraints)){ + if(!is.null(preferences) && is.null(constraints)) { constraints <- generate_constraints(preferences, n_items) - } else if (is.null(constraints)){ + } else if (is.null(constraints)) { constraints <- list() } @@ -352,7 +352,7 @@ compute_mallows <- function(rankings = NULL, logz_list <- prepare_partition_function(logz_estimate, metric, n_items) - if(save_ind_clus){ + if(save_ind_clus) { abort <- readline( prompt = paste(nmc, "csv files will be saved in your current working directory.", "Proceed? (yes/no): ")) @@ -387,7 +387,7 @@ compute_mallows <- function(rankings = NULL, save_ind_clus = save_ind_clus ) - if(verbose){ + if(verbose) { print("Metropolis-Hastings algorithm completed. Post-processing data.") } diff --git a/R/compute_mallows_mixtures.R b/R/compute_mallows_mixtures.R index 68c03dbd..5dedcde9 100644 --- a/R/compute_mallows_mixtures.R +++ b/R/compute_mallows_mixtures.R @@ -22,18 +22,18 @@ #' #' @example /inst/examples/compute_mallows_mixtures_example.R #' -compute_mallows_mixtures <- function(n_clusters, ..., cl = NULL){ +compute_mallows_mixtures <- function(n_clusters, ..., cl = NULL) { stopifnot(is.numeric(n_clusters)) stopifnot(is.null(cl) || inherits(cl, "cluster")) - if(is.null(cl)){ + if(is.null(cl)) { models <- lapply(n_clusters, function(x) { compute_mallows(..., n_clusters = x) }) } else { args <- list(...) parallel::clusterExport(cl = cl, varlist = "args", envir = environment()) - models <- parallel::parLapply(cl = cl, X = n_clusters, fun = function(x){ + models <- parallel::parLapply(cl = cl, X = n_clusters, fun = function(x) { args$n_clusters <- x do.call(compute_mallows, args) }) diff --git a/R/compute_posterior_intervals.R b/R/compute_posterior_intervals.R index 1523f0dd..677e206d 100644 --- a/R/compute_posterior_intervals.R +++ b/R/compute_posterior_intervals.R @@ -50,7 +50,7 @@ compute_posterior_intervals.BayesMallows <- function( ) { stopifnot(class(model_fit) == "BayesMallows") - if(is.null(burnin)){ + if(is.null(burnin)) { stop("Please specify the burnin.") } @@ -60,14 +60,14 @@ compute_posterior_intervals.BayesMallows <- function( df <- dplyr::filter(model_fit[[parameter]], .data$iteration > burnin) - if(parameter == "alpha" || parameter == "cluster_probs"){ + if(parameter == "alpha" || parameter == "cluster_probs") { df <- dplyr::group_by(df, .data$cluster) class(df) <- c("posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") df <- .compute_posterior_intervals(df, parameter, level, decimals) - } else if(parameter == "rho"){ + } else if(parameter == "rho") { decimals <- 0 df <- dplyr::group_by(df, .data$cluster, .data$item) class(df) <- c("posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") @@ -129,7 +129,7 @@ compute_posterior_intervals.SMCMallows <- function( .compute_posterior_intervals.posterior_BayesMallows <- function( df, parameter, level, decimals, discrete = FALSE, ... -){ +) { dplyr::do(df, { format <- paste0("%.", decimals, "f") @@ -163,7 +163,7 @@ compute_posterior_intervals.SMCMallows <- function( hpdi <- HDInterval::hdi(.data$value, credMass = level, allowSplit = TRUE) hpdi[] <- sprintf(format, hpdi) - if(is.matrix(hpdi)){ + if(is.matrix(hpdi)) { # Discontinous case hpdi <- paste(apply(hpdi, 1, function(x) paste0("[", x[[1]], ",", x[[2]], "]"))) } else { diff --git a/R/estimate_partition_function.R b/R/estimate_partition_function.R index 3e3f6c30..b77f6cc7 100644 --- a/R/estimate_partition_function.R +++ b/R/estimate_partition_function.R @@ -52,23 +52,23 @@ estimate_partition_function <- function(method = "importance_sampling", alpha_vector, n_items, metric, nmc, degree, n_iterations, K, cl = NULL, - seed = NULL){ + seed = NULL) { stopifnot(degree < length(alpha_vector)) - if(method == "importance_sampling"){ - if(!is.null(cl)){ + if(method == "importance_sampling") { + if(!is.null(cl)) { # Split nmc into each cluster nmc_vec <- rep(floor(nmc / length(cl)), length(cl)) i <- 1 - while(sum(nmc_vec) != nmc){ + while(sum(nmc_vec) != nmc) { nmc_vec[i] <- nmc_vec[i] + 1 if(i > length(cl)) break() } parallel::clusterExport(cl, c("alpha_vector", "n_items", "metric", "seed"), envir = environment()) - estimates <- parallel::parLapply(cl, nmc_vec, function(x){ + estimates <- parallel::parLapply(cl, nmc_vec, function(x) { if(!is.null(seed)) set.seed(seed) compute_importance_sampling_estimate(alpha_vector = alpha_vector, n_items = n_items, metric = metric, nmc = x) @@ -88,7 +88,7 @@ estimate_partition_function <- function(method = "importance_sampling", alpha = alpha_vector, log_z = log_z ) - } else if(method == "asymptotic"){ + } else if(method == "asymptotic") { stopifnot(metric %in% c("footrule", "spearman")) estimate <- dplyr::tibble( diff --git a/R/expected_dist.R b/R/expected_dist.R index 130f8b84..b14ab5d5 100644 --- a/R/expected_dist.R +++ b/R/expected_dist.R @@ -10,32 +10,32 @@ #' #' @example /inst/examples/expected_dist_example.R -expected_dist <- function(alpha, n_items, metric){ +expected_dist <- function(alpha, n_items, metric) { - if(n_items < 1 | floor(n_items) != n_items){ + if(n_items < 1 | floor(n_items) != n_items) { stop("Number of items must be a positive integer") } # Scale alpha to parametrization used alpha <- alpha / n_items - if(alpha < 0){ + if(alpha < 0) { stop("alpha must be a non-negative value") }else{ - if(metric == "kendall"){ + if(metric == "kendall") { out <- exp_d_tau(alpha, n_items) } - if(metric == "cayley"){ + if(metric == "cayley") { out <- exp_d_cay(alpha, n_items) } - if(metric == "hamming"){ + if(metric == "hamming") { out <- exp_d_ham(alpha, n_items) } - if(metric %in% c("ulam", "footrule", "spearman")){ + if(metric %in% c("ulam", "footrule", "spearman")) { pfd <- dplyr::filter(partition_function_data, .data$metric == !!metric, .data$n_items == !!n_items, .data$type == "cardinalities") - if(nrow(pfd) == 0){ + if(nrow(pfd) == 0) { stop("Given number of items currently not available for the specified metric") } else{ card <- pfd$values[[1]] diff --git a/R/generate_constraints.R b/R/generate_constraints.R index a905c098..1ad5a3f6 100644 --- a/R/generate_constraints.R +++ b/R/generate_constraints.R @@ -23,7 +23,7 @@ #' #' @example /inst/examples/generate_constraints_example.R #' -generate_constraints <- function(preferences, n_items, cl = NULL){ +generate_constraints <- function(preferences, n_items, cl = NULL) { stopifnot(is.null(cl) || inherits(cl, "cluster")) @@ -40,7 +40,7 @@ generate_constraints <- function(preferences, n_items, cl = NULL){ -constraint_fun <- function(x, n_items){ +constraint_fun <- function(x, n_items) { # Find out which items are constrained constrained_items <- unique(c(x[["bottom_item"]], x[["top_item"]])) diff --git a/R/generate_initial_ranking.R b/R/generate_initial_ranking.R index 2709d684..84fcf19e 100644 --- a/R/generate_initial_ranking.R +++ b/R/generate_initial_ranking.R @@ -69,25 +69,25 @@ generate_initial_ranking <- function(tc, n_items = max(tc[, c("bottom_item", "top_item")]), cl = NULL, shuffle_unranked = FALSE, random = FALSE, - random_limit = 8L){ + random_limit = 8L) { - if(!("BayesMallowsTC" %in% class(tc))){ + if(!("BayesMallowsTC" %in% class(tc))) { stop("tc must be an object returned from generate_transitive_closure") } stopifnot(is.null(cl) || inherits(cl, "cluster")) - if(n_items > random_limit && random){ + if(n_items > random_limit && random) { stop(paste("Number of items exceeds the limit for generation of random permutations,\n", "modify the random_limit argument to override this.\n")) } - if(n_items < max(tc[, c("bottom_item", "top_item")])){ + if(n_items < max(tc[, c("bottom_item", "top_item")])) { stop("Too few items specified. Please see documentation Note about labeling of items.\n") } prefs <- split(tc[, c("bottom_item", "top_item"), drop = FALSE], tc$assessor) - if(is.null(cl)){ + if(is.null(cl)) { prefs <- lapply(prefs, function(x, y, sr, r) create_ranks(as.matrix(x), y, sr, r), n_items, shuffle_unranked, random) } else { @@ -99,15 +99,15 @@ generate_initial_ranking <- function(tc, do.call(rbind, prefs) } -create_ranks <- function(mat, n_items, shuffle_unranked, random){ +create_ranks <- function(mat, n_items, shuffle_unranked, random) { - if(!random){ + if(!random) { g <- igraph::graph_from_edgelist(mat) g <- as.integer(igraph::topo_sort(g)) all_items <- seq(from = 1, to = n_items, by = 1) - if(!shuffle_unranked){ + if(!shuffle_unranked) { # Add unranked elements outside of the range at the end g_final <- c(g, setdiff(all_items, g)) @@ -127,7 +127,7 @@ create_ranks <- function(mat, n_items, shuffle_unranked, random){ mat <- matrix(r, nrow = 1) } else{ graph <- list() - for(i in seq_len(n_items)){ + for(i in seq_len(n_items)) { graph[[i]] <- unique(mat[mat[, "top_item"] == i, "bottom_item"]) } indegree_init <- rep(0, n_items) diff --git a/R/generate_transitive_closure.R b/R/generate_transitive_closure.R index 7457c782..1f1a8d3f 100644 --- a/R/generate_transitive_closure.R +++ b/R/generate_transitive_closure.R @@ -41,12 +41,12 @@ #' #' @example /inst/examples/generate_transitive_closure_example.R #' -generate_transitive_closure <- function(df, cl = NULL){ +generate_transitive_closure <- function(df, cl = NULL) { stopifnot(is.null(cl) || inherits(cl, "cluster")) prefs <- split(df[, c("bottom_item", "top_item"), drop = FALSE], df$assessor) - if(is.null(cl)){ + if(is.null(cl)) { prefs <- mapply(function(x, y) cbind(y, .generate_transitive_closure(cbind(x$bottom_item, x$top_item))), prefs, unique(df$assessor), SIMPLIFY = FALSE) } else { @@ -66,7 +66,7 @@ generate_transitive_closure <- function(df, cl = NULL){ "bottom_item" = "top_item", "top_item" = "bottom_item")) - if(nrow(check) > 0){ + if(nrow(check) > 0) { print("Inconsistent rankings:") print(check) stop("Cannot compute transitive closure. Please run compute_mallows with error_model='bernoulli'.") @@ -83,7 +83,7 @@ generate_transitive_closure <- function(df, cl = NULL){ #' @param mat A matrix in which column 1 is the lower ranked item and column 2 is the #' upper ranked item. #' @keywords internal -.generate_transitive_closure <- function(mat){ +.generate_transitive_closure <- function(mat) { # This line was an answer to StackOverflow question 51794127 my_set <- do.call(sets::set, apply(mat, 1, sets::as.tuple)) diff --git a/R/lik_db_mix.R b/R/lik_db_mix.R index 2cbe591f..1b5eb976 100644 --- a/R/lik_db_mix.R +++ b/R/lik_db_mix.R @@ -31,23 +31,23 @@ #' @example inst/examples/lik_db_mix_example.R #' lik_db_mix <- function(rho, alpha, weights, metric, - rankings, obs_freq = NULL, log = FALSE){ + rankings, obs_freq = NULL, log = FALSE) { - if(!is.matrix(rankings)){ + if(!is.matrix(rankings)) { rankings <- matrix(rankings, nrow = 1) } - if(!is.null(obs_freq)){ - if(nrow(rankings) != length(obs_freq)){ + if(!is.null(obs_freq)) { + if(nrow(rankings) != length(obs_freq)) { stop("obs_freq must be of same length as the number of rows in rankings") } } - if(!is.matrix(rho)){ + if(!is.matrix(rho)) { rho <- matrix(rho, nrow = 1) } - if(is.null(obs_freq)){ + if(is.null(obs_freq)) { obs_freq <- rep(1, nrow(rankings)) } diff --git a/R/misc.R b/R/misc.R index ce8c2461..584adeac 100644 --- a/R/misc.R +++ b/R/misc.R @@ -13,9 +13,9 @@ NULL #' @return TRUE if vec is a permutation #' @keywords internal validate_permutation <- function(vec) { - if(!any(is.na(vec))){ + if(!any(is.na(vec))) { return(all(sort(vec) == seq_along(vec))) - } else if(all(is.na(vec))){ + } else if(all(is.na(vec))) { return(TRUE) } else { return(all(vec[!is.na(vec)] <= length(vec)) && @@ -33,16 +33,16 @@ scalefun <- function(x) sprintf("%d", as.integer(x)) -prepare_partition_function <- function(logz_estimate, metric, n_items){ +prepare_partition_function <- function(logz_estimate, metric, n_items) { # First, has the user supplied an estimate? - if(!is.null(logz_estimate)){ + if(!is.null(logz_estimate)) { return(list(cardinalities = NULL, logz_estimate = logz_estimate)) } # Second, do we have a sequence? relevant_params <- dplyr::filter(partition_function_data, .data$n_items == !!n_items, .data$metric == !!metric, .data$type == "cardinalities") - if(nrow(relevant_params) == 1){ + if(nrow(relevant_params) == 1) { return(list(cardinalities = unlist(relevant_params$values), logz_estimate = NULL)) } @@ -50,12 +50,12 @@ prepare_partition_function <- function(logz_estimate, metric, n_items){ relevant_params <- dplyr::filter(partition_function_data, .data$n_items == !!n_items, .data$metric == !!metric, .data$type == "importance_sampling") - if(nrow(relevant_params) == 1){ + if(nrow(relevant_params) == 1) { return(list(cardinalities = NULL, logz_estimate = unlist(relevant_params$values))) } # Fourth, is it the Ulam distance? - if(metric == "ulam"){ + if(metric == "ulam") { return(list( cardinalities = unlist(lapply(0:(n_items - 1), function(x) PerMallows::count.perms(perm.length = n_items, dist.value = x, dist.name = "ulam"))) @@ -63,7 +63,7 @@ prepare_partition_function <- function(logz_estimate, metric, n_items){ } # Fifth, can we compute the partition function in our C++ code? - if(metric %in% c("cayley", "hamming", "kendall")){ + if(metric %in% c("cayley", "hamming", "kendall")) { return(list(cardinalities = NULL, logz_estimate = NULL)) } @@ -109,14 +109,14 @@ fill_single_entries <- function(data) { ## Source: https://stackoverflow.com/questions/11095992/generating-all-distinct-permutations-of-a-list-in-r -permutations <- function(n){ - if(n == 1){ +permutations <- function(n) { + if(n == 1) { return(matrix(1)) } else { sp <- permutations(n - 1) p <- nrow(sp) A <- matrix(nrow = n * p, ncol = n) - for(i in 1:n){ + for(i in 1:n) { A[(i - 1) * p + 1:p, ] <- cbind(i, sp + (sp >= i)) } return(A) diff --git a/R/misc_expected_dist.R b/R/misc_expected_dist.R index 705e899d..0a6e813b 100644 --- a/R/misc_expected_dist.R +++ b/R/misc_expected_dist.R @@ -5,8 +5,8 @@ #/' #/' @return Expected value of the Kendall metric under the Mallows rank model with the Kendall distance. -exp_d_tau <- function(alpha, n_items){ - if(alpha > 0){ +exp_d_tau <- function(alpha, n_items) { + if(alpha > 0) { idx <- seq(from = 1, to = n_items, by = 1) out <- n_items * exp(-alpha) / (1 - exp(-alpha)) - sum((idx * exp(-idx * alpha)) / (1 - exp(-idx * alpha))) @@ -27,7 +27,7 @@ exp_d_tau <- function(alpha, n_items){ #/' #/' @return Expected value of the Cayley metric under the Mallows rank model with the Cayley distance. -exp_d_cay <- function(alpha, n_items){ +exp_d_cay <- function(alpha, n_items) { idx <- seq(from = 1, to = n_items - 1, by = 1) out <- sum(idx / (idx + exp(alpha))) return(out) @@ -40,7 +40,7 @@ exp_d_cay <- function(alpha, n_items){ #/' #/' @return Expected value of the Hamming metric under the Mallows rank model with the Hamming distance. -exp_d_ham <- function(alpha, n_items){ +exp_d_ham <- function(alpha, n_items) { idx <- seq(from = 0, to = n_items, by = 1) out <- n_items - exp(alpha) * sum(((exp(alpha) - 1)^idx[-(n_items + 1)]) / base::factorial(idx[-(n_items + 1)])) / @@ -56,7 +56,7 @@ exp_d_ham <- function(alpha, n_items){ #/' @return Expected value of the Ulam metric under the Mallows rank model with the Ulam distance. # The function based on the command from the PerMallows package is slow because it has to generate the distance frequencies. -exp_d_ulam <- function(alpha, n_items){ # for n_items<=95 +exp_d_ulam <- function(alpha, n_items) { # for n_items<=95 idx <- seq(from = 0, to = n_items - 1, by = 1) pfd <- partition_function_data card <- pfd$values[pfd$metric == "ulam"][[n_items]] @@ -77,7 +77,7 @@ exp_d_ulam <- function(alpha, n_items){ # for n_items<=95 #/' #/' @return Expected value of the Footrule metric under the Mallows rank model with the Footrule distance. -exp_d_foot <- function(alpha, n_items){ # for n_items<=50 +exp_d_foot <- function(alpha, n_items) { # for n_items<=50 idx <- seq(0, floor(n_items^2 / 2), by = 2) pfd <- partition_function_data card <- pfd$values[pfd$metric == "footrule"][[n_items]] diff --git a/R/misc_likelihood.R b/R/misc_likelihood.R index 4809d78f..8c14e8e7 100644 --- a/R/misc_likelihood.R +++ b/R/misc_likelihood.R @@ -11,12 +11,12 @@ #/' @return The log-likelihood value corresponding to one or more observed rankings under the Mallows rank model with distance specified by the \code{metric} argument. #/' -log_lik_db <- function(rho, alpha, metric, rankings, obs_freq){ +log_lik_db <- function(rho, alpha, metric, rankings, obs_freq) { N <- sum(obs_freq) n_items <- ncol(rankings) - if(metric %in% c("kendall", "cayley", "hamming")){ + if(metric %in% c("kendall", "cayley", "hamming")) { log_lik <- -( alpha * rank_dist_sum( @@ -29,11 +29,11 @@ log_lik_db <- function(rho, alpha, metric, rankings, obs_freq){ ) } - if(metric %in% c( "ulam", "footrule", "spearman")){ + if(metric %in% c( "ulam", "footrule", "spearman")) { pfd <- dplyr::filter(partition_function_data, .data$metric == !!metric, .data$n_items == !!n_items, .data$type == "cardinalities") - if(nrow(pfd) == 0){ + if(nrow(pfd) == 0) { stop("Given number of items currently not available for the specified metric") } else{ card <- pfd$values[[1]] @@ -71,13 +71,13 @@ log_lik_db <- function(rho, alpha, metric, rankings, obs_freq){ #/' log_lik_db_mix <- function(rho, alpha, weights, metric, - rankings, obs_freq){ + rankings, obs_freq) { L <- length(obs_freq) n_clusters <- length(weights) temp <- matrix(NA, nrow = n_clusters, ncol = L) - for(l in seq_len(L)){ - for(g in seq_len(n_clusters)){ + for(l in seq_len(L)) { + for(g in seq_len(n_clusters)) { temp[g, l] <- exp(log_lik_db( rho = rho[g, ], alpha = alpha[g], metric = metric, rankings = rankings[ l, , drop = FALSE], diff --git a/R/plot.BayesMallows.R b/R/plot.BayesMallows.R index c27265ac..ff6ba50a 100644 --- a/R/plot.BayesMallows.R +++ b/R/plot.BayesMallows.R @@ -24,12 +24,12 @@ #' #' @example /inst/examples/plot.BayesMallows_example.R #' -plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = NULL, ...){ +plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = NULL, ...) { # Note, the first argument must be named x, otherwise R CMD CHECK will # issue a warning. This is because plot.BayesMallows must have the same # required arguments as graphics::plot. - if(is.null(burnin)){ + if(is.null(burnin)) { stop("Please specify the burnin.") } if(x$nmc <= burnin) stop("burnin must be <= nmc") @@ -44,7 +44,7 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = ggplot2::xlab(expression(alpha)) + ggplot2::ylab("Posterior density") - if(x$n_clusters > 1){ + if(x$n_clusters > 1) { p <- p + ggplot2::facet_wrap(~ .data$cluster, scales = "free_x") } @@ -52,14 +52,14 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = } else if(parameter == "rho") { - if(is.null(items) && x$n_items > 5){ + if(is.null(items) && x$n_items > 5) { message("Items not provided by user. Picking 5 at random.") items <- sample.int(x$n_items, 5) } else if (is.null(items) && x$n_items > 0) { items <- seq.int(from = 1, to = x$n_items) } - if(!is.character(items)){ + if(!is.character(items)) { items <- x$items[items] } @@ -78,14 +78,14 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = ggplot2::xlab("rank") + ggplot2::ylab("Posterior probability") - if(x$n_clusters == 1){ + if(x$n_clusters == 1) { p <- p + ggplot2::facet_wrap(~ .data$item) } else { p <- p + ggplot2::facet_wrap(~ .data$cluster + .data$item) } return(p) - } else if(parameter == "cluster_probs"){ + } else if(parameter == "cluster_probs") { df <- dplyr::filter(x$cluster_probs, .data$iteration > burnin) ggplot2::ggplot(df, ggplot2::aes(x = .data$value)) + @@ -94,9 +94,9 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = ggplot2::ylab("Posterior density") + ggplot2::facet_wrap(~ .data$cluster) - } else if(parameter == "cluster_assignment"){ + } else if(parameter == "cluster_assignment") { - if(is.null(x$cluster_assignment)){ + if(is.null(x$cluster_assignment)) { stop("Please rerun compute_mallows with save_clus = TRUE") } @@ -123,7 +123,7 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = } else if(parameter == "theta") { - if(is.null(x$theta)){ + if(is.null(x$theta)) { stop("Please run compute_mallows with error_model = 'bernoulli'.") } diff --git a/R/plot_elbow.R b/R/plot_elbow.R index 3b599244..5809234d 100644 --- a/R/plot_elbow.R +++ b/R/plot_elbow.R @@ -23,21 +23,21 @@ #' #' @example /inst/examples/compute_mallows_mixtures_example.R #' -plot_elbow <- function(..., burnin = NULL){ +plot_elbow <- function(..., burnin = NULL) { # Put the models into a list. These are typically fitted with different number of clusters models <- list(...) # Taking into account the case where the user has entered a list of models - if(length(models) == 1 && !(class(models[[1]]) == "BayesMallows")){ + if(length(models) == 1 && !(class(models[[1]]) == "BayesMallows")) { models <- models[[1]] } df <- do.call(rbind, lapply(models, function(x) { stopifnot(class(x) == "BayesMallows") - if(!("burnin" %in% names(x))){ - if(is.null(burnin)){ + if(!("burnin" %in% names(x))) { + if(is.null(burnin)) { stop("burnin not provided") } else { x$burnin <- burnin diff --git a/R/plot_top_k.R b/R/plot_top_k.R index 71f6f5dc..a6952d8d 100644 --- a/R/plot_top_k.R +++ b/R/plot_top_k.R @@ -25,7 +25,7 @@ #' plot_top_k <- function(model_fit, burnin = model_fit$burnin, k = 3, - rel_widths = c(model_fit$n_clusters, 10)){ + rel_widths = c(model_fit$n_clusters, 10)) { validate_top_k(model_fit, burnin) @@ -38,7 +38,7 @@ plot_top_k <- function(model_fit, burnin = model_fit$burnin, rho <- dplyr::summarise(rho, prob = dplyr::n() / n_samples, .groups = "drop") # Find the complete set of items per cluster - rho <- do.call(rbind, lapply(split(rho, f = rho$cluster), function(dd){ + rho <- do.call(rbind, lapply(split(rho, f = rho$cluster), function(dd) { dd2 <- merge(dd, expand.grid(item = unique(rho$item)), by = "item", all = TRUE) dd2$cluster[is.na(dd2$cluster)] <- unique(dd$cluster) @@ -48,7 +48,7 @@ plot_top_k <- function(model_fit, burnin = model_fit$burnin, # Sort the items according to probability in Cluster 1 item_ordering <- compute_consensus(model_fit, type = "CP", burnin = burnin) - if(model_fit$n_clusters > 1){ + if(model_fit$n_clusters > 1) { item_ordering <- rev(item_ordering[item_ordering$cluster == "Cluster 1", ]$item) } else { item_ordering <- rev(item_ordering$item) @@ -57,7 +57,7 @@ plot_top_k <- function(model_fit, burnin = model_fit$burnin, rho <- dplyr::mutate(rho, item = factor(.data$item, levels = unique(item_ordering))) # Trick to make the plot look nicer - if(model_fit$n_clusters == 1){ + if(model_fit$n_clusters == 1) { rho <- dplyr::mutate(rho, cluster = "") } @@ -82,7 +82,7 @@ plot_top_k <- function(model_fit, burnin = model_fit$burnin, ggplot2::xlab(expression(rho)) + ggplot2::theme(legend.position = "none") - if(model_fit$n_clusters > 1){ + if(model_fit$n_clusters > 1) { rho_plot <- rho_plot + ggplot2::facet_wrap(~ .data$cluster) } diff --git a/R/predict_top_k.R b/R/predict_top_k.R index 3eb96eeb..746861ee 100644 --- a/R/predict_top_k.R +++ b/R/predict_top_k.R @@ -23,7 +23,7 @@ #' @seealso \code{\link{plot_top_k}} #' predict_top_k <- function(model_fit, burnin = model_fit$burnin, - k = 3){ + k = 3) { validate_top_k(model_fit, burnin) .predict_top_k(model_fit, burnin, k) @@ -31,7 +31,7 @@ predict_top_k <- function(model_fit, burnin = model_fit$burnin, -.predict_top_k <- function(model_fit, burnin, k){ +.predict_top_k <- function(model_fit, burnin, k) { rankings <- dplyr::filter(model_fit$augmented_data, .data$iteration > burnin, .data$value <= k) n_samples <- length(unique(rankings$iteration)) @@ -39,7 +39,7 @@ predict_top_k <- function(model_fit, burnin = model_fit$burnin, rankings <- dplyr::group_by(rankings, .data$assessor, .data$item) rankings <- dplyr::summarise(rankings, prob = dplyr::n() / n_samples, .groups = "drop") - do.call(rbind, lapply(split(rankings, f = rankings$assessor), function(dd){ + do.call(rbind, lapply(split(rankings, f = rankings$assessor), function(dd) { dd2 <- merge(dd, expand.grid(item = unique(rankings$item)), by = "item", all = TRUE) dd2$assessor[is.na(dd2$assessor)] <- unique(dd$assessor) @@ -50,13 +50,13 @@ predict_top_k <- function(model_fit, burnin = model_fit$burnin, } -validate_top_k <- function(model_fit, burnin){ - if(is.null(burnin)){ +validate_top_k <- function(model_fit, burnin) { + if(is.null(burnin)) { stop("Please specify the burnin.") } stopifnot(burnin < model_fit$nmc) - if(!exists("augmented_data", model_fit)){ + if(!exists("augmented_data", model_fit)) { stop("model_fit must have element augmented_data. Please set save_aug = TRUE in compute_mallows in order to create a top-k plot.") } diff --git a/R/print.BayesMallows.R b/R/print.BayesMallows.R index f29a1dce..1eac4632 100644 --- a/R/print.BayesMallows.R +++ b/R/print.BayesMallows.R @@ -11,7 +11,7 @@ #' @export #' #' -print.BayesMallows <- function(x, ...){ +print.BayesMallows <- function(x, ...) { # Note, the first argument must be named x, otherwise R CMD CHECK will # issue a warning. This is because print.BayesMallows must have the same # required arguments as base::print. diff --git a/R/print.BayesMallowsMixtures.R b/R/print.BayesMallowsMixtures.R index 8c489c92..6bfd95aa 100644 --- a/R/print.BayesMallowsMixtures.R +++ b/R/print.BayesMallowsMixtures.R @@ -11,7 +11,7 @@ #' @export #' #' -print.BayesMallowsMixtures <- function(x, ...){ +print.BayesMallowsMixtures <- function(x, ...) { # Note, the first argument must be named x, otherwise R CMD CHECK will # issue a warning. This is because print.BayesMallowsMixtures must have the same # required arguments as base::print. diff --git a/R/rank_conversion.R b/R/rank_conversion.R index 0918bc4a..5d11e150 100644 --- a/R/rank_conversion.R +++ b/R/rank_conversion.R @@ -42,13 +42,13 @@ NULL #' @describeIn rank_conversion Convert from ordering to ranking. #' @export -create_ranking <- function(orderings){ +create_ranking <- function(orderings) { # Check that it is a permutation - if(is.vector(orderings)){ + if(is.vector(orderings)) { stopifnot(validate_permutation(orderings)) return(order(orderings)) - } else if(is.matrix(orderings)){ + } else if(is.matrix(orderings)) { n_items <- ncol(orderings) # Convert to list, for easier functional programming @@ -57,7 +57,7 @@ create_ranking <- function(orderings){ # Check that matrix contains permutations check <- lapply(orderings, validate_permutation) - if(!Reduce(`&&`, check)){ + if(!Reduce(`&&`, check)) { stop(paste("orderings must contain proper permutations. Problem row(s):", which(!check))) } @@ -84,7 +84,7 @@ create_ranking <- function(orderings){ #' @describeIn rank_conversion Convert from ranking to ordering. #' @export -create_ordering <- function(rankings){ +create_ordering <- function(rankings) { create_ranking(rankings) } diff --git a/R/rank_distance.R b/R/rank_distance.R index 4d464366..bb778d02 100644 --- a/R/rank_distance.R +++ b/R/rank_distance.R @@ -20,15 +20,15 @@ #' @references \insertAllCited #' #' @example /inst/examples/rank_distance_example.R -rank_distance <- function(rankings, rho, metric, obs_freq = 1){ +rank_distance <- function(rankings, rho, metric, obs_freq = 1) { - if(!is.matrix(rankings)){ + if(!is.matrix(rankings)) { rankings <- matrix(rankings, nrow = 1) } stopifnot(length(obs_freq) == 1 || length(obs_freq) == nrow(rankings)) - if(length(obs_freq) == 1){ + if(length(obs_freq) == 1) { obs_freq <- rep(obs_freq, nrow(rankings)) } diff --git a/R/rank_freq_distr.R b/R/rank_freq_distr.R index 5904986e..32e736f5 100644 --- a/R/rank_freq_distr.R +++ b/R/rank_freq_distr.R @@ -13,9 +13,9 @@ #' #' @example /inst/examples/rank_freq_distr_example.R #' -rank_freq_distr <- function(rankings){ +rank_freq_distr <- function(rankings) { - if(!is.matrix(rankings)){ + if(!is.matrix(rankings)) { rankings <- matrix(rankings, nrow = 1) } diff --git a/R/sample_mallows.R b/R/sample_mallows.R index e9a114a6..cf895a7b 100644 --- a/R/sample_mallows.R +++ b/R/sample_mallows.R @@ -51,19 +51,19 @@ sample_mallows <- function(rho0, alpha0, n_samples, max_lag = 1000L) { - if(!(validate_permutation(rho0) && sum(is.na(rho0)) == 0)){ + if(!(validate_permutation(rho0) && sum(is.na(rho0)) == 0)) { stop("rho0 must be a proper ranking with no missing values.") } - if(diagnostic && n_samples == 1){ + if(diagnostic && n_samples == 1) { stop("Must have more than one samples to create diagnostic plots") - } else if(n_samples <= 0){ + } else if(n_samples <= 0) { stop("n_samples must be positive.") } n_items <- length(rho0) - if(diagnostic){ + if (diagnostic) { internal_burnin <- 0 internal_thinning <- 1 internal_n_samples <- burnin + n_samples * thinning @@ -84,8 +84,8 @@ sample_mallows <- function(rho0, alpha0, n_samples, obs_freq = rep(1, internal_n_samples) )) - if(diagnostic){ - if(is.null(items_to_plot) && n_items > 5){ + if(diagnostic) { + if(is.null(items_to_plot) && n_items > 5) { message("Items not provided by user. Picking 5 at random.") items_to_plot <- sample.int(n_items, 5) } else { @@ -97,7 +97,7 @@ sample_mallows <- function(rho0, alpha0, n_samples, lag.max = max_lag, plot = FALSE, demean = TRUE) names(autocorr) <- items_to_plot - autocorr <- do.call(rbind, Map(function(x, xnm){ + autocorr <- do.call(rbind, Map(function(x, xnm) { data.frame( item = xnm, acf = x$acf[, 1, 1], diff --git a/R/smc_post_processing_functions.R b/R/smc_post_processing_functions.R index 9a86c14f..24f0c174 100644 --- a/R/smc_post_processing_functions.R +++ b/R/smc_post_processing_functions.R @@ -11,7 +11,7 @@ smc_processing <- function(output, colnames = NULL) { df <- data.frame(data = output) # if colnames are specified, then incorporate them - if(is.null(colnames)){ + if(is.null(colnames)) { n_items <- ncol(df) cletters <- rep(c("Item"), times = n_items) cindexes <- (c(1:n_items)) @@ -172,11 +172,11 @@ compute_posterior_intervals_alpha <- function(output, nmc, burnin, verbose=FALSE #' @param items Either a vector of item names, or a #' vector of indices. If NULL, five items are selected randomly. #' @export -plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = NULL){ +plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = NULL) { n_items <- dim(output)[2] - if(is.null(items) && n_items > 5){ + if(is.null(items) && n_items > 5) { message("Items not provided by user or more than 5 items in a ranking. Picking 5 at random.") items <- sample(1:n_items, 5, replace = F) items <- sort(items) @@ -189,14 +189,14 @@ plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = # do smc processing here smc_plot <- smc_processing(output = output, colnames = colnames) - if(!is.character(items)){ + if(!is.character(items)) { items <- unique(smc_plot$item)[items] } iteration <- rep(c(1:nmc), times = n_items) df <- cbind(iteration, smc_plot) - if(C == 1){ + if(C == 1) { df <- cbind(cluster = "Cluster 1", df) } @@ -220,7 +220,7 @@ plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = ggplot2::xlab("rank") + ggplot2::ylab("Posterior probability") - if(C == 1){ + if(C == 1) { p <- p + ggplot2::facet_wrap(~ .data$item) } else { p <- p + ggplot2::facet_wrap(~ .data$cluster + .data$item) diff --git a/R/tidy_mcmc.R b/R/tidy_mcmc.R index 34c6f847..376a474b 100644 --- a/R/tidy_mcmc.R +++ b/R/tidy_mcmc.R @@ -1,4 +1,4 @@ -tidy_mcmc <- function(fit){ +tidy_mcmc <- function(fit) { fit <- tidy_rho(fit) fit <- tidy_alpha(fit) @@ -14,7 +14,7 @@ tidy_mcmc <- function(fit){ -tidy_rho <- function(fit){ +tidy_rho <- function(fit) { # Tidy rho rho_dims <- dim(fit$rho) # Item1, Item2, Item3, ...., Item1, Item2, Item3 @@ -47,7 +47,7 @@ tidy_rho <- function(fit){ return(fit) } -tidy_alpha <- function(fit){ +tidy_alpha <- function(fit) { # Tidy alpha alpha_dims <- dim(fit$alpha) # Cluster1, Cluster2, ..., Cluster1, Cluster2 @@ -76,11 +76,11 @@ tidy_alpha <- function(fit){ return(fit) } -tidy_cluster_assignment <- function(fit){ +tidy_cluster_assignment <- function(fit) { # Tidy cluster assignment - if(fit$save_clus){ - if(fit$n_clusters > 1){ + if(fit$save_clus) { + if(fit$n_clusters > 1) { cluster_dims <- dim(fit$cluster_assignment) value <- paste("Cluster", c(fit$cluster_assignment)) } else { @@ -114,9 +114,9 @@ tidy_cluster_assignment <- function(fit){ return(fit) } -tidy_cluster_probabilities <- function(fit){ +tidy_cluster_probabilities <- function(fit) { # Tidy cluster probabilities - if(fit$n_clusters > 1){ + if(fit$n_clusters > 1) { clusprob_dims <- dim(fit$cluster_probs) value <- c(fit$cluster_probs) } else { @@ -149,9 +149,9 @@ tidy_cluster_probabilities <- function(fit){ } -tidy_wcd <- function(fit){ +tidy_wcd <- function(fit) { # Tidy the within-cluster distances, or delete the empty matrix - if(fit$include_wcd){ + if(fit$include_wcd) { wcd_dims <- dim(fit$within_cluster_distance) value <- c(fit$within_cluster_distance) @@ -183,9 +183,9 @@ tidy_wcd <- function(fit){ return(fit) } -tidy_augmented_data <- function(fit){ +tidy_augmented_data <- function(fit) { # Tidy augmented data, or delete - if(fit$save_aug){ + if(fit$save_aug) { augdata_dims <- dim(fit$augmented_data) @@ -217,10 +217,10 @@ tidy_augmented_data <- function(fit){ } -tidy_augmentation_acceptance <- function(fit){ +tidy_augmentation_acceptance <- function(fit) { # Augmentation acceptance - if(fit$any_missing || fit$augpair){ + if(fit$any_missing || fit$augpair) { fit$aug_acceptance <- dplyr::tibble(acceptance_rate = c(fit$aug_acceptance)) fit$aug_acceptance <- dplyr::mutate(fit$aug_acceptance, assessor = dplyr::row_number()) @@ -235,10 +235,10 @@ tidy_augmentation_acceptance <- function(fit){ -tidy_error_probability <- function(fit){ +tidy_error_probability <- function(fit) { theta_length <- length(fit$theta) - if(theta_length > 0){ + if(theta_length > 0) { fit$theta <- dplyr::tibble( iteration = seq(from = 1, to = theta_length, by = 1), value = c(fit$theta) diff --git a/tests/testthat/test-compute_mallows.R b/tests/testthat/test-compute_mallows.R index d58a4cbf..cb8eb3c7 100644 --- a/tests/testthat/test-compute_mallows.R +++ b/tests/testthat/test-compute_mallows.R @@ -66,7 +66,7 @@ test_that("compute_mallows with missing data works", { test_that("compute_mallows runs with the right distances", { - for(metric in c("footrule", "spearman", "cayley", "kendall", "ulam", "hamming")){ + for(metric in c("footrule", "spearman", "cayley", "kendall", "ulam", "hamming")) { expect_s3_class(compute_mallows(potato_visual, metric = metric, nmc = 3), "BayesMallows") } @@ -120,7 +120,7 @@ test_that("compute_mallows treats obs_freq properly", { # Next, we create a new hypthetical beach_preferences dataframe where each # assessor is replicated 1-4 times - beach_pref_rep <- do.call(rbind, lapply(split(beach_small, f = seq_len(nrow(beach_small))), function(dd){ + beach_pref_rep <- do.call(rbind, lapply(split(beach_small, f = seq_len(nrow(beach_small))), function(dd) { ret <- merge( dd, data.frame(new_assessor = seq_len(obs_freq[dd$assessor])), diff --git a/tests/testthat/test-distance_function.R b/tests/testthat/test-distance_function.R index 3a5a756e..4737f326 100644 --- a/tests/testthat/test-distance_function.R +++ b/tests/testthat/test-distance_function.R @@ -1,6 +1,6 @@ context("Testing computation of distance") # Brute force formula -check_dist <- function(n, fun){ +check_dist <- function(n, fun) { # Generate all permutations perm <- permutations(n) @@ -9,7 +9,7 @@ check_dist <- function(n, fun){ # Loop over some n values test_that("footrule distance is correct", { - for(n in c(2, 3, 5)){ + for(n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "footrule") @@ -20,7 +20,7 @@ test_that("footrule distance is correct", { # Loop over some n values test_that("Spearman distance is correct", { - for(n in c(2, 3, 5)){ + for(n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "spearman") @@ -31,7 +31,7 @@ test_that("Spearman distance is correct", { # Loop over some n values test_that("Kendall distance is correct", { - for(n in c(2, 3, 5)){ + for(n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "kendall") @@ -44,7 +44,7 @@ test_that("Kendall distance is correct", { # Loop over some n values test_that("Cayley distance is correct", { - for(n in c(2, 3, 5)){ + for(n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "cayley") @@ -57,7 +57,7 @@ test_that("Cayley distance is correct", { # Loop over some n values test_that("Hamming distance is correct", { - for(n in c(2, 3, 5)){ + for(n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "hamming") @@ -70,7 +70,7 @@ test_that("Hamming distance is correct", { # Loop over some n values test_that("Ulam distance is correct", { - for(n in c(2, 3, 5)){ + for(n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "ulam") diff --git a/tests/testthat/test-estimate_partition_function.R b/tests/testthat/test-estimate_partition_function.R index 289830fa..c73f5d43 100644 --- a/tests/testthat/test-estimate_partition_function.R +++ b/tests/testthat/test-estimate_partition_function.R @@ -4,7 +4,7 @@ context("Testing function estimate_partition_function") test_that( "estimate_partition_function fails for wrong asymptotic metrics", { - for(metric in c("cayley", "hamming", "kendall", "ulam")){ + for(metric in c("cayley", "hamming", "kendall", "ulam")) { expect_error( estimate_partition_function(method = "asymptotic", alpha_vector = seq(from = 1, to = 2, by = .1), diff --git a/tests/testthat/test-misc_cpp.R b/tests/testthat/test-misc_cpp.R index b72d099a..f44870b2 100644 --- a/tests/testthat/test-misc_cpp.R +++ b/tests/testthat/test-misc_cpp.R @@ -31,7 +31,7 @@ test_that( n <- 10000L probs <- c(0.1, 0.2, 0.7) values <- integer(n) - for(i in seq(1L, n, 1L)){ + for(i in seq(1L, n, 1L)) { values[[i]] <- BayesMallows:::sample_int(probs) } freqs <- table(values) / length(values) @@ -42,7 +42,7 @@ test_that( # TRUE, due to the randomness in sampling skip_on_cran() diff <- abs(probs - freqs) - for(i in 1:3){ + for(i in 1:3) { expect_lt(diff[[i]], 0.02) } diff --git a/tests/testthat/test-partition_function.R b/tests/testthat/test-partition_function.R index 55459687..8a57dd8e 100644 --- a/tests/testthat/test-partition_function.R +++ b/tests/testthat/test-partition_function.R @@ -2,7 +2,7 @@ context("Testing computation of partition functions") # Brute force formula -check_log_zn <- function(n, alpha, metric){ +check_log_zn <- function(n, alpha, metric) { # Generate all permutations perm <- permutations(n) @@ -39,8 +39,8 @@ test_that("footrule partition function is correct", { footrule_sequence <- dplyr::filter(BayesMallows:::partition_function_data, metric == "footrule", type == "cardinalities")$values - for(n in c(1, 2, 3, 5)){ - for(alpha in c(0.001, 0.1, 1)){ + for(n in c(1, 2, 3, 5)) { + for(alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, cardinalities = footrule_sequence[[n]], metric = "footrule"), @@ -54,8 +54,8 @@ test_that("Spearman partition function is correct", { spearman_sequence <- dplyr::filter(BayesMallows:::partition_function_data, metric == "spearman", type == "cardinalities")$values - for(n in c(1, 2, 3)){ - for(alpha in c(0.001, 0.1, 1)){ + for(n in c(1, 2, 3)) { + for(alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, cardinalities = spearman_sequence[[n]], metric = "spearman"), @@ -66,8 +66,8 @@ test_that("Spearman partition function is correct", { test_that("Kendall partition function is correct", { - for(n in c(1, 2, 3)){ - for(alpha in c(0.001, 0.1, 1)){ + for(n in c(1, 2, 3)) { + for(alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, metric = "kendall"), check_log_zn(n, alpha, "kendall") @@ -76,8 +76,8 @@ test_that("Kendall partition function is correct", { }}) test_that("Cayley partition function is correct", { - for(n in c(1, 2, 3)){ - for(alpha in c(0.001, 0.1, 1)){ + for(n in c(1, 2, 3)) { + for(alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, metric = "cayley"), check_log_zn(n, alpha, "cayley") @@ -87,8 +87,8 @@ test_that("Cayley partition function is correct", { test_that("Hamming partition function is correct", { - for(n in c(1, 2, 3)){ - for(alpha in c(0.001, 0.1, 1)){ + for(n in c(1, 2, 3)) { + for(alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, metric = "hamming"), check_log_zn(n, alpha, "hamming") @@ -100,8 +100,8 @@ test_that("Ulam partition function is correct", { ulam_sequence <- dplyr::filter(BayesMallows:::partition_function_data, metric == "ulam", type == "cardinalities")$values - for(n in c(1, 2, 3)){ - for(alpha in c(0.001, 0.1, 1)){ + for(n in c(1, 2, 3)) { + for(alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, cardinalities = ulam_sequence[[n]], @@ -134,7 +134,7 @@ test_that("estimate_partition_function runs in parallel", { nmc = 20, degree = degree) - lapply(1:2, function(x){ + lapply(1:2, function(x) { cl <- parallel::makeCluster(x) fit <- estimate_partition_function(method = "importance_sampling", alpha_vector = alpha_vector, diff --git a/tests/testthat/test-rank_freq_distr.R b/tests/testthat/test-rank_freq_distr.R index e02fa213..1f6e2643 100644 --- a/tests/testthat/test-rank_freq_distr.R +++ b/tests/testthat/test-rank_freq_distr.R @@ -25,7 +25,7 @@ test_that("rank_freq_distr works", { ) set.seed(9988) - rows <- unlist(Map(function(x, y){ + rows <- unlist(Map(function(x, y) { rep(y, each = x) }, x = sample(20:100, nrow(potato_visual), replace = TRUE), diff --git a/tests/testthat/test-sample_mallows.R b/tests/testthat/test-sample_mallows.R index 74fd2d64..53e81538 100644 --- a/tests/testthat/test-sample_mallows.R +++ b/tests/testthat/test-sample_mallows.R @@ -8,7 +8,7 @@ rho0 <- seq(from = 1, to = n_items, by = 1) # Set the scale alpha0 <- 10 -for(m in c("footrule", "spearman", "cayley", "hamming", "kendall", "ulam")){ +for(m in c("footrule", "spearman", "cayley", "hamming", "kendall", "ulam")) { samples <- sample_mallows(rho0 = rho0, alpha0 = alpha0, n_samples = 100, burnin = 1000, thinning = 1000, metric = m, leap_size = 1) test_that( From 1e66f1eced516ead16cf697518bfc8cabd1d6dd8 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 13:01:09 +0100 Subject: [PATCH 37/41] Replaced single quotes with double --- R/generate_initial_ranking.R | 4 ++-- tests/testthat/test-smc_mallows_new_item_rank.R | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/R/generate_initial_ranking.R b/R/generate_initial_ranking.R index 84fcf19e..0dda3aa0 100644 --- a/R/generate_initial_ranking.R +++ b/R/generate_initial_ranking.R @@ -138,8 +138,8 @@ create_ranks <- function(mat, n_items, shuffle_unranked, random) { discovered <- rep(FALSE, n_items) path <- numeric() - stdout <- vector('character') - con <- textConnection('stdout', 'wr', local = TRUE) + stdout <- vector("character") + con <- textConnection("stdout", "wr", local = TRUE) sink(con) all_topological_sorts(graph, path, discovered, n_items) sink() diff --git a/tests/testthat/test-smc_mallows_new_item_rank.R b/tests/testthat/test-smc_mallows_new_item_rank.R index cee4603f..400375ba 100644 --- a/tests/testthat/test-smc_mallows_new_item_rank.R +++ b/tests/testthat/test-smc_mallows_new_item_rank.R @@ -1,4 +1,4 @@ -context('SMC new user and item rank combined') +context("SMC new user and item rank combined") # a simpler example to test ==================================================== set.seed(101) From 0df35265ffc8bc4cd76142290536093fe3895d11 Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 13:08:55 +0100 Subject: [PATCH 38/41] Adjusting space before left parenthesis --- R/all_topological_sorts.R | 6 +- R/assess_convergence.R | 44 ++++++------- R/assign_cluster.R | 8 +-- R/compute_consensus.R | 34 +++++----- R/compute_mallows.R | 66 +++++++++---------- R/compute_mallows_mixtures.R | 2 +- R/compute_posterior_intervals.R | 12 ++-- R/estimate_partition_function.R | 14 ++-- R/expected_dist.R | 14 ++-- R/generate_constraints.R | 2 +- R/generate_initial_ranking.R | 14 ++-- R/generate_transitive_closure.R | 4 +- R/lik_db_mix.R | 12 ++-- R/misc.R | 18 ++--- R/misc_expected_dist.R | 6 +- R/misc_likelihood.R | 18 ++--- R/plot.BayesMallows.R | 26 ++++---- R/plot_elbow.R | 10 +-- R/plot_top_k.R | 6 +- R/predict_top_k.R | 4 +- R/print.BayesMallows.R | 2 +- R/print.BayesMallowsMixtures.R | 2 +- R/rank_conversion.R | 8 +-- R/rank_distance.R | 4 +- R/rank_freq_distr.R | 2 +- R/sample_mallows.R | 10 +-- R/smc_post_processing_functions.R | 12 ++-- R/tidy_mcmc.R | 14 ++-- tests/testthat/test-compute_mallows.R | 2 +- tests/testthat/test-distance_function.R | 12 ++-- .../test-estimate_partition_function.R | 2 +- tests/testthat/test-misc_cpp.R | 4 +- tests/testthat/test-partition_function.R | 40 +++++------ tests/testthat/test-sample_mallows.R | 2 +- 34 files changed, 218 insertions(+), 218 deletions(-) diff --git a/R/all_topological_sorts.R b/R/all_topological_sorts.R index 2b8ad533..10480660 100644 --- a/R/all_topological_sorts.R +++ b/R/all_topological_sorts.R @@ -3,8 +3,8 @@ all_topological_sorts <- function(graph, path, discovered, n_items) { flag <- FALSE - for(i in seq_len(n_items)) { - if(attr(graph, "indegree")[[i]] == 0 && !discovered[[i]]) { + for (i in seq_len(n_items)) { + if (attr(graph, "indegree")[[i]] == 0 && !discovered[[i]]) { attr(graph, "indegree")[graph[[i]]] <- attr(graph, "indegree")[graph[[i]]] - 1 path <- c(path, i) @@ -18,5 +18,5 @@ all_topological_sorts <- function(graph, path, discovered, n_items) { flag <- TRUE } } - if(length(path) == n_items) print(path) + if (length(path) == n_items) print(path) } diff --git a/R/assess_convergence.R b/R/assess_convergence.R index 16d81f57..e90d1ec6 100644 --- a/R/assess_convergence.R +++ b/R/assess_convergence.R @@ -31,11 +31,11 @@ assess_convergence <- function(model_fit, parameter = "alpha", items = NULL, stopifnot(inherits(model_fit, "BayesMallows") || inherits(model_fit, "BayesMallowsMixtures")) - if(parameter == "alpha") { - if(inherits(model_fit, "BayesMallows")) { + if (parameter == "alpha") { + if (inherits(model_fit, "BayesMallows")) { m <- model_fit$alpha trace_alpha(m, FALSE) - } else if(inherits(model_fit, "BayesMallowsMixtures")) { + } else if (inherits(model_fit, "BayesMallowsMixtures")) { m <- do.call(rbind, lapply(model_fit, function(x) { dplyr::mutate(x$alpha, cluster = as.character(.data$cluster), @@ -44,24 +44,24 @@ assess_convergence <- function(model_fit, parameter = "alpha", items = NULL, trace_alpha(m, TRUE) } - } else if(parameter == "rho") { - if(inherits(model_fit, "BayesMallows")) { + } else if (parameter == "rho") { + if (inherits(model_fit, "BayesMallows")) { trace_rho(model_fit, items) - } else if(inherits(model_fit, "BayesMallowsMixtures")) { + } else if (inherits(model_fit, "BayesMallowsMixtures")) { cowplot::plot_grid(plotlist = lapply(model_fit, trace_rho, clusters = TRUE, items = items)) } - } else if(parameter == "Rtilde") { + } else if (parameter == "Rtilde") { - if(inherits(model_fit, "BayesMallows")) { + if (inherits(model_fit, "BayesMallows")) { trace_rtilde(model_fit, items, assessors) - } else if(inherits(model_fit, "BayesMallowsMixtures")) { + } else if (inherits(model_fit, "BayesMallowsMixtures")) { stop("Trace plots of augmented data not supported for BayesMallowsMixtures. Please rerun each component k using the k-th list element.") } } else if (parameter == "cluster_probs") { - if(inherits(model_fit, "BayesMallows")) { + if (inherits(model_fit, "BayesMallows")) { m <- model_fit$cluster_probs - } else if(inherits(model_fit, "BayesMallowsMixtures")) { + } else if (inherits(model_fit, "BayesMallowsMixtures")) { m <- do.call(rbind, lapply(model_fit, function(x) { dplyr::mutate(x$cluster_probs, cluster = as.character(.data$cluster), @@ -83,7 +83,7 @@ trace_alpha <- function(m, clusters) { ggplot2::xlab("Iteration") + ggplot2::ylab(expression(alpha)) - if(!clusters) { + if (!clusters) { p <- p + ggplot2::geom_line() } else { p <- p + @@ -99,14 +99,14 @@ trace_alpha <- function(m, clusters) { trace_rho <- function(model_fit, items, clusters = model_fit$n_clusters > 1) { - if(is.null(items) && model_fit$n_items > 5) { + if (is.null(items) && model_fit$n_items > 5) { message("Items not provided by user. Picking 5 at random.") items <- sample.int(model_fit$n_items, 5) } else if (is.null(items) && model_fit$n_items > 0) { items <- seq.int(from = 1, to = model_fit$n_items) } - if(!is.character(items)) { + if (!is.character(items)) { items <- model_fit$items[items] } @@ -118,7 +118,7 @@ trace_rho <- function(model_fit, items, clusters = model_fit$n_clusters > 1) { ggplot2::xlab("Iteration") + ggplot2::ylab(expression(rho)) - if(clusters) { + if (clusters) { p <- p + ggplot2::facet_wrap(ggplot2::vars(.data$cluster)) } @@ -129,29 +129,29 @@ trace_rho <- function(model_fit, items, clusters = model_fit$n_clusters > 1) { trace_rtilde <- function(model_fit, items, assessors, ...) { - if(!model_fit$save_aug) { + if (!model_fit$save_aug) { stop("Please rerun with compute_mallows with save_aug = TRUE") } - if(is.null(items) && model_fit$n_items > 5) { + if (is.null(items) && model_fit$n_items > 5) { message("Items not provided by user. Picking 5 at random.") items <- sample.int(model_fit$n_items, 5) } else if (is.null(items) && model_fit$n_items > 0) { items <- seq.int(from = 1, to = model_fit$n_items) } - if(is.null(assessors) && model_fit$n_assessors > 5) { + if (is.null(assessors) && model_fit$n_assessors > 5) { message("Assessors not provided by user. Picking 5 at random.") assessors <- sample.int(model_fit$n_assessors, 5) } else if (is.null(assessors) && model_fit$n_assessors > 0) { assessors <- seq.int(from = 1, to = model_fit$n_assessors) - } else if(!is.null(assessors)) { - if(length(setdiff(assessors, seq(1, model_fit$n_assessors, 1))) > 0) { + } else if (!is.null(assessors)) { + if (length(setdiff(assessors, seq(1, model_fit$n_assessors, 1))) > 0) { stop("assessors vector must contain numeric indices between 1 and the number of assessors") } } - if(is.factor(model_fit$augmented_data$item) && is.numeric(items)) { + if (is.factor(model_fit$augmented_data$item) && is.numeric(items)) { items <- levels(model_fit$augmented_data$item)[items] } df <- dplyr::filter(model_fit$augmented_data, @@ -185,7 +185,7 @@ trace_cluster_probs <- function(m) { trace_theta <- function(model_fit) { - if(is.null(model_fit$theta) || length(model_fit$theta) == 0) { + if (is.null(model_fit$theta) || length(model_fit$theta) == 0) { stop("Theta not available. Run compute_mallows with error_model = 'bernoulli'.") } # Create the diagnostic plot for theta diff --git a/R/assign_cluster.R b/R/assign_cluster.R index f1268448..11f646a9 100644 --- a/R/assign_cluster.R +++ b/R/assign_cluster.R @@ -32,10 +32,10 @@ #' assign_cluster <- function(model_fit, burnin = model_fit$burnin, soft = TRUE, expand = FALSE) { - if(is.null(burnin)) { + if (is.null(burnin)) { stop("Please specify the burnin.") } - if(is.null(model_fit$cluster_assignment)) { + if (is.null(model_fit$cluster_assignment)) { stop("Rerun compute_mallows with save_clus=TRUE.") } stopifnot(burnin < model_fit$nmc) @@ -53,7 +53,7 @@ assign_cluster <- function(model_fit, burnin = model_fit$burnin, soft = TRUE, ex df <- dplyr::ungroup(df) df <- dplyr::rename(df, cluster = .data$value) - if(expand) { + if (expand) { df <- do.call(rbind, lapply(split(df, f = df$assessor), function(dd) { dd2 <- merge(dd, expand.grid(cluster = unique(df$cluster)), by = "cluster", all = TRUE) @@ -78,7 +78,7 @@ assign_cluster <- function(model_fit, burnin = model_fit$burnin, soft = TRUE, ex # Join map back onto df df <- dplyr::inner_join(df, map, by = "assessor") - if(!soft) { + if (!soft) { df <- dplyr::filter(df, .data$cluster == .data$map_cluster) df <- dplyr::select(df, -.data$cluster) } diff --git a/R/compute_consensus.R b/R/compute_consensus.R index bbf770c1..871cc35d 100644 --- a/R/compute_consensus.R +++ b/R/compute_consensus.R @@ -44,18 +44,18 @@ compute_consensus.BayesMallows <- function( model_fit, type = "CP", burnin = model_fit$burnin, parameter = "rho", assessors = 1L, ... ) { - if(is.null(burnin)) { + if (is.null(burnin)) { stop("Please specify the burnin.") } stopifnot(burnin < model_fit$nmc) stopifnot(class(model_fit) == "BayesMallows") - if(parameter == "Rtilde" && !inherits(model_fit$augmented_data, "data.frame")) { + if (parameter == "Rtilde" && !inherits(model_fit$augmented_data, "data.frame")) { stop("For augmented ranks, please refit model with option 'save_aug = TRUE'.") } - if(parameter == "rho") { + if (parameter == "rho") { # Filter out the pre-burnin iterations df <- dplyr::filter(model_fit$rho, .data$iteration > burnin) @@ -71,14 +71,14 @@ compute_consensus.BayesMallows <- function( class(df) <- c("consensus_BayesMallows", "tbl_df", "tbl", "data.frame") - df <- if(type == "CP") { + df <- if (type == "CP") { .compute_cp_consensus(df) - } else if(type == "MAP") { + } else if (type == "MAP") { .compute_map_consensus(df) } - } else if(parameter == "Rtilde") { + } else if (parameter == "Rtilde") { # Filter out the pre-burnin iterations and get the right assessors df <- dplyr::filter(model_fit$augmented_data, .data$iteration > burnin, .data$assessor %in% assessors) @@ -96,13 +96,13 @@ compute_consensus.BayesMallows <- function( df <- dplyr::rename(df, cluster = "assessor") class(df) <- c("consensus_BayesMallows", "tbl_df", "tbl", "data.frame") - df <- if(type == "CP") { + df <- if (type == "CP") { .compute_cp_consensus(df) - } else if(type == "MAP") { + } else if (type == "MAP") { .compute_map_consensus(df) } - if("cluster" %in% names(df)) { + if ("cluster" %in% names(df)) { df <- dplyr::rename(df, assessor = "cluster") } @@ -168,7 +168,7 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { # If there is only one cluster, we drop the cluster column - if(length(unique(df$cluster)) == 1) { + if (length(unique(df$cluster)) == 1) { df <- dplyr::select(df, -.data$cluster) } @@ -176,7 +176,7 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { } .compute_cp_consensus.consensus_SMCMallows <- function(model_fit, burnin) { - if(is.null(burnin)) { + if (is.null(burnin)) { stop("Please specify the burnin.") } @@ -184,7 +184,7 @@ compute_consensus.consensus_SMCMallows <- function(model_fit, type, burnin) { # Filter out the pre-burnin iterations - if(burnin != 0) { + if (burnin != 0) { df <- dplyr::filter(model_fit, .data$iteration > burnin) } else { df <- model_fit @@ -245,7 +245,7 @@ find_cpc.consensus_BayesMallows <- function(group_df) { ) n_items <- max(group_df$value) - for(i in seq(from = 1, to = n_items, by = 1)) { + for (i in seq(from = 1, to = n_items, by = 1)) { # Filter out the relevant rows tmp_df <- dplyr::filter(group_df, .data$value == i) @@ -277,7 +277,7 @@ find_cpc.consensus_SMCMallows <- function(group_df) { cumprob = numeric() ) n_items <- max(group_df$value) - for(i in seq(from = 1, to = n_items, by = 1)) { + for (i in seq(from = 1, to = n_items, by = 1)) { # Filter out the relevant rows tmp_df <- dplyr::filter(group_df, group_df$value == i) @@ -344,7 +344,7 @@ find_cpc.consensus_SMCMallows <- function(group_df) { # Sort according to cluster and ranking df <- dplyr::arrange(df, .data$cluster, .data$map_ranking) - if(length(unique(df$cluster)) == 1) { + if (length(unique(df$cluster)) == 1) { df <- dplyr::select(df, -.data$cluster) } @@ -356,11 +356,11 @@ find_cpc.consensus_SMCMallows <- function(group_df) { #AS: added one extra line of code to resolve of the issues in #118 with plotting too many rows in compute_rho_consensus .compute_map_consensus.consensus_SMCMallows <- function(model_fit, burnin = model_fit$burnin) { - if(is.null(burnin)) { + if (is.null(burnin)) { stop("Please specify the burnin.") } - if(burnin != 0) { + if (burnin != 0) { df <- dplyr::filter(model_fit, .data$iteration > burnin) } else { df <- model_fit diff --git a/R/compute_mallows.R b/R/compute_mallows.R index da0756c3..7cc7ce6d 100644 --- a/R/compute_mallows.R +++ b/R/compute_mallows.R @@ -239,15 +239,15 @@ compute_mallows <- function(rankings = NULL, seed = NULL ) { - if(!is.null(seed)) set.seed(seed) + if (!is.null(seed)) set.seed(seed) # Check if there are NAs in rankings, if it is provided - if(!is.null(rankings)) { - if(na_action == "fail" && any(is.na(rankings))) { + if (!is.null(rankings)) { + if (na_action == "fail" && any(is.na(rankings))) { stop("rankings matrix contains NA values") } - if(na_action == "omit" && any(is.na(rankings))) { + if (na_action == "omit" && any(is.na(rankings))) { keeps <- apply(rankings, 1, function(x) !any(is.na(x))) print(paste("Omitting", sum(keeps), "rows from rankings due to NA values")) rankings <- rankings[keeps, , drop = FALSE] @@ -255,46 +255,46 @@ compute_mallows <- function(rankings = NULL, } # Check that at most one of rankings and preferences is set - if(is.null(rankings) && is.null(preferences)) { + if (is.null(rankings) && is.null(preferences)) { stop("Either rankings or preferences (or both) must be provided.") } - if(is.null(preferences) && !is.null(error_model)) { + if (is.null(preferences) && !is.null(error_model)) { stop("Error model requires preferences to be set.") } # Check if obs_freq are provided - if(!is.null(obs_freq)) { - if(is.null(rankings)) { + if (!is.null(obs_freq)) { + if (is.null(rankings)) { stop("rankings matrix must be provided when obs_freq are provided") } - if(nrow(rankings) != length(obs_freq)) { + if (nrow(rankings) != length(obs_freq)) { stop("obs_freq must be of same length as the number of rows in rankings") } } - if(!swap_leap > 0) stop("swap_leap must be strictly positive") - if(nmc <= 0) stop("nmc must be strictly positive") + if (!swap_leap > 0) stop("swap_leap must be strictly positive") + if (nmc <= 0) stop("nmc must be strictly positive") # Check that we do not jump over all alphas - if(alpha_jump >= nmc) stop("alpha_jump must be strictly smaller than nmc") + if (alpha_jump >= nmc) stop("alpha_jump must be strictly smaller than nmc") # Check that we do not jump over all rhos - if(rho_thinning >= nmc) stop("rho_thinning must be strictly smaller than nmc") - if(aug_thinning >= nmc) stop("aug_thinning must be strictly smaller than nmc") + if (rho_thinning >= nmc) stop("rho_thinning must be strictly smaller than nmc") + if (aug_thinning >= nmc) stop("aug_thinning must be strictly smaller than nmc") - if(lambda <= 0) stop("exponential rate parameter lambda must be strictly positive") + if (lambda <= 0) stop("exponential rate parameter lambda must be strictly positive") # Check that all rows of rankings are proper permutations - if(!is.null(rankings) && validate_rankings && !all(apply(rankings, 1, validate_permutation))) { + if (!is.null(rankings) && validate_rankings && !all(apply(rankings, 1, validate_permutation))) { stop("invalid permutations provided in rankings matrix") } # Deal with pairwise comparisons. Generate rankings compatible with them. - if(!is.null(preferences) && is.null(error_model)) { + if (!is.null(preferences) && is.null(error_model)) { - if(!inherits(preferences, "BayesMallowsTC")) { + if (!inherits(preferences, "BayesMallowsTC")) { message("Generating transitive closure of preferences.") # Make sure the preference columns are double preferences <- dplyr::mutate(preferences, @@ -304,15 +304,15 @@ compute_mallows <- function(rankings = NULL, preferences <- generate_transitive_closure(preferences) } - if(is.null(rankings)) { + if (is.null(rankings)) { message("Generating initial ranking.") rankings <- generate_initial_ranking(preferences) } - } else if(!is.null(error_model)) { + } else if (!is.null(error_model)) { stopifnot(error_model == "bernoulli") n_items <- max(c(preferences$bottom_item, preferences$top_item)) n_assessors <- length(unique(preferences$assessor)) - if(is.null(rankings)) { + if (is.null(rankings)) { rankings <- replicate(n_assessors, sample(x = n_items, size = n_items), simplify = "numeric") rankings <- matrix(rankings, ncol = n_items, nrow = n_assessors, byrow = TRUE) } @@ -322,41 +322,41 @@ compute_mallows <- function(rankings = NULL, n_items <- ncol(rankings) # If any row of rankings has only one missing value, replace it with the implied ranking - if(any(is.na(rankings))) { + if (any(is.na(rankings))) { dn <- dimnames(rankings) rankings <- lapply(split(rankings, f = seq_len(nrow(rankings))), function(x) { - if(sum(is.na(x)) == 1) x[is.na(x)] <- setdiff(1:length(x), x) + if (sum(is.na(x)) == 1) x[is.na(x)] <- setdiff(1:length(x), x) return(x) }) rankings <- do.call(rbind, rankings) dimnames(rankings) <- dn } - if(!is.null(rho_init)) { - if(!validate_permutation(rho_init)) stop("rho_init must be a proper permutation") - if(!(sum(is.na(rho_init)) == 0)) stop("rho_init cannot have missing values") - if(length(rho_init) != n_items) stop("rho_init must have the same number of items as implied by rankings or preferences") + if (!is.null(rho_init)) { + if (!validate_permutation(rho_init)) stop("rho_init must be a proper permutation") + if (!(sum(is.na(rho_init)) == 0)) stop("rho_init cannot have missing values") + if (length(rho_init) != n_items) stop("rho_init must have the same number of items as implied by rankings or preferences") rho_init <- matrix(rho_init, ncol = 1) } # Generate the constraint set - if(!is.null(preferences) && is.null(constraints)) { + if (!is.null(preferences) && is.null(constraints)) { constraints <- generate_constraints(preferences, n_items) } else if (is.null(constraints)) { constraints <- list() } - if(is.null(obs_freq)) obs_freq <- rep(1, nrow(rankings)) + if (is.null(obs_freq)) obs_freq <- rep(1, nrow(rankings)) logz_list <- prepare_partition_function(logz_estimate, metric, n_items) - if(save_ind_clus) { + if (save_ind_clus) { abort <- readline( prompt = paste(nmc, "csv files will be saved in your current working directory.", "Proceed? (yes/no): ")) - if(tolower(abort) %in% c("n", "no")) stop() + if (tolower(abort) %in% c("n", "no")) stop() } # Transpose rankings to get samples along columns, since we typically want @@ -387,7 +387,7 @@ compute_mallows <- function(rankings = NULL, save_ind_clus = save_ind_clus ) - if(verbose) { + if (verbose) { print("Metropolis-Hastings algorithm completed. Post-processing data.") } @@ -407,7 +407,7 @@ compute_mallows <- function(rankings = NULL, fit$save_clus <- save_clus # Add names of item - if(!is.null(colnames(rankings))) { + if (!is.null(colnames(rankings))) { fit$items <- colnames(rankings) } else { fit$items <- paste("Item", seq(from = 1, to = nrow(fit$rho), by = 1)) diff --git a/R/compute_mallows_mixtures.R b/R/compute_mallows_mixtures.R index 5dedcde9..83fcd52f 100644 --- a/R/compute_mallows_mixtures.R +++ b/R/compute_mallows_mixtures.R @@ -26,7 +26,7 @@ compute_mallows_mixtures <- function(n_clusters, ..., cl = NULL) { stopifnot(is.numeric(n_clusters)) stopifnot(is.null(cl) || inherits(cl, "cluster")) - if(is.null(cl)) { + if (is.null(cl)) { models <- lapply(n_clusters, function(x) { compute_mallows(..., n_clusters = x) }) diff --git a/R/compute_posterior_intervals.R b/R/compute_posterior_intervals.R index 677e206d..c3113a7f 100644 --- a/R/compute_posterior_intervals.R +++ b/R/compute_posterior_intervals.R @@ -50,7 +50,7 @@ compute_posterior_intervals.BayesMallows <- function( ) { stopifnot(class(model_fit) == "BayesMallows") - if(is.null(burnin)) { + if (is.null(burnin)) { stop("Please specify the burnin.") } @@ -60,14 +60,14 @@ compute_posterior_intervals.BayesMallows <- function( df <- dplyr::filter(model_fit[[parameter]], .data$iteration > burnin) - if(parameter == "alpha" || parameter == "cluster_probs") { + if (parameter == "alpha" || parameter == "cluster_probs") { df <- dplyr::group_by(df, .data$cluster) class(df) <- c("posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") df <- .compute_posterior_intervals(df, parameter, level, decimals) - } else if(parameter == "rho") { + } else if (parameter == "rho") { decimals <- 0 df <- dplyr::group_by(df, .data$cluster, .data$item) class(df) <- c("posterior_BayesMallows", "grouped_df", "tbl_df", "tbl", "data.frame") @@ -77,7 +77,7 @@ compute_posterior_intervals.BayesMallows <- function( df <- dplyr::ungroup(df) - if(model_fit$n_clusters == 1) df <- dplyr::select(df, -.data$cluster) + if (model_fit$n_clusters == 1) df <- dplyr::select(df, -.data$cluster) return(df) } @@ -136,7 +136,7 @@ compute_posterior_intervals.SMCMallows <- function( posterior_mean <- round(base::mean(.data$value), decimals) posterior_median <- round(stats::median(.data$value), decimals) - if(discrete) { + if (discrete) { df <- dplyr::group_by(.data, .data$value) df <- dplyr::summarise(df, n = dplyr::n()) @@ -163,7 +163,7 @@ compute_posterior_intervals.SMCMallows <- function( hpdi <- HDInterval::hdi(.data$value, credMass = level, allowSplit = TRUE) hpdi[] <- sprintf(format, hpdi) - if(is.matrix(hpdi)) { + if (is.matrix(hpdi)) { # Discontinous case hpdi <- paste(apply(hpdi, 1, function(x) paste0("[", x[[1]], ",", x[[2]], "]"))) } else { diff --git a/R/estimate_partition_function.R b/R/estimate_partition_function.R index b77f6cc7..f2c34af2 100644 --- a/R/estimate_partition_function.R +++ b/R/estimate_partition_function.R @@ -56,27 +56,27 @@ estimate_partition_function <- function(method = "importance_sampling", stopifnot(degree < length(alpha_vector)) - if(method == "importance_sampling") { - if(!is.null(cl)) { + if (method == "importance_sampling") { + if (!is.null(cl)) { # Split nmc into each cluster nmc_vec <- rep(floor(nmc / length(cl)), length(cl)) i <- 1 - while(sum(nmc_vec) != nmc) { + while (sum(nmc_vec) != nmc) { nmc_vec[i] <- nmc_vec[i] + 1 - if(i > length(cl)) break() + if (i > length(cl)) break } parallel::clusterExport(cl, c("alpha_vector", "n_items", "metric", "seed"), envir = environment()) estimates <- parallel::parLapply(cl, nmc_vec, function(x) { - if(!is.null(seed)) set.seed(seed) + if (!is.null(seed)) set.seed(seed) compute_importance_sampling_estimate(alpha_vector = alpha_vector, n_items = n_items, metric = metric, nmc = x) }) log_z <- rowMeans(do.call(cbind, estimates)) } else { - if(!is.null(seed)) set.seed(seed) + if (!is.null(seed)) set.seed(seed) log_z <- as.numeric( compute_importance_sampling_estimate( alpha_vector = alpha_vector, n_items = n_items, @@ -88,7 +88,7 @@ estimate_partition_function <- function(method = "importance_sampling", alpha = alpha_vector, log_z = log_z ) - } else if(method == "asymptotic") { + } else if (method == "asymptotic") { stopifnot(metric %in% c("footrule", "spearman")) estimate <- dplyr::tibble( diff --git a/R/expected_dist.R b/R/expected_dist.R index b14ab5d5..d026f1e5 100644 --- a/R/expected_dist.R +++ b/R/expected_dist.R @@ -12,30 +12,30 @@ expected_dist <- function(alpha, n_items, metric) { - if(n_items < 1 | floor(n_items) != n_items) { + if (n_items < 1 | floor(n_items) != n_items) { stop("Number of items must be a positive integer") } # Scale alpha to parametrization used alpha <- alpha / n_items - if(alpha < 0) { + if (alpha < 0) { stop("alpha must be a non-negative value") }else{ - if(metric == "kendall") { + if (metric == "kendall") { out <- exp_d_tau(alpha, n_items) } - if(metric == "cayley") { + if (metric == "cayley") { out <- exp_d_cay(alpha, n_items) } - if(metric == "hamming") { + if (metric == "hamming") { out <- exp_d_ham(alpha, n_items) } - if(metric %in% c("ulam", "footrule", "spearman")) { + if (metric %in% c("ulam", "footrule", "spearman")) { pfd <- dplyr::filter(partition_function_data, .data$metric == !!metric, .data$n_items == !!n_items, .data$type == "cardinalities") - if(nrow(pfd) == 0) { + if (nrow(pfd) == 0) { stop("Given number of items currently not available for the specified metric") } else{ card <- pfd$values[[1]] diff --git a/R/generate_constraints.R b/R/generate_constraints.R index 1ad5a3f6..15976e83 100644 --- a/R/generate_constraints.R +++ b/R/generate_constraints.R @@ -31,7 +31,7 @@ generate_constraints <- function(preferences, n_items, cl = NULL) { # one list element per assessor constraints <- split(preferences[, c("bottom_item", "top_item"), drop = FALSE], preferences$assessor) - if(is.null(cl)) { + if (is.null(cl)) { lapply(constraints, constraint_fun, n_items) } else { parallel::parLapply(cl = cl, X = constraints, fun = constraint_fun, n_items) diff --git a/R/generate_initial_ranking.R b/R/generate_initial_ranking.R index 0dda3aa0..85c0f34d 100644 --- a/R/generate_initial_ranking.R +++ b/R/generate_initial_ranking.R @@ -72,22 +72,22 @@ generate_initial_ranking <- function(tc, random_limit = 8L) { - if(!("BayesMallowsTC" %in% class(tc))) { + if (!("BayesMallowsTC" %in% class(tc))) { stop("tc must be an object returned from generate_transitive_closure") } stopifnot(is.null(cl) || inherits(cl, "cluster")) - if(n_items > random_limit && random) { + if (n_items > random_limit && random) { stop(paste("Number of items exceeds the limit for generation of random permutations,\n", "modify the random_limit argument to override this.\n")) } - if(n_items < max(tc[, c("bottom_item", "top_item")])) { + if (n_items < max(tc[, c("bottom_item", "top_item")])) { stop("Too few items specified. Please see documentation Note about labeling of items.\n") } prefs <- split(tc[, c("bottom_item", "top_item"), drop = FALSE], tc$assessor) - if(is.null(cl)) { + if (is.null(cl)) { prefs <- lapply(prefs, function(x, y, sr, r) create_ranks(as.matrix(x), y, sr, r), n_items, shuffle_unranked, random) } else { @@ -101,13 +101,13 @@ generate_initial_ranking <- function(tc, create_ranks <- function(mat, n_items, shuffle_unranked, random) { - if(!random) { + if (!random) { g <- igraph::graph_from_edgelist(mat) g <- as.integer(igraph::topo_sort(g)) all_items <- seq(from = 1, to = n_items, by = 1) - if(!shuffle_unranked) { + if (!shuffle_unranked) { # Add unranked elements outside of the range at the end g_final <- c(g, setdiff(all_items, g)) @@ -127,7 +127,7 @@ create_ranks <- function(mat, n_items, shuffle_unranked, random) { mat <- matrix(r, nrow = 1) } else{ graph <- list() - for(i in seq_len(n_items)) { + for (i in seq_len(n_items)) { graph[[i]] <- unique(mat[mat[, "top_item"] == i, "bottom_item"]) } indegree_init <- rep(0, n_items) diff --git a/R/generate_transitive_closure.R b/R/generate_transitive_closure.R index 1f1a8d3f..1835af8d 100644 --- a/R/generate_transitive_closure.R +++ b/R/generate_transitive_closure.R @@ -46,7 +46,7 @@ generate_transitive_closure <- function(df, cl = NULL) { stopifnot(is.null(cl) || inherits(cl, "cluster")) prefs <- split(df[, c("bottom_item", "top_item"), drop = FALSE], df$assessor) - if(is.null(cl)) { + if (is.null(cl)) { prefs <- mapply(function(x, y) cbind(y, .generate_transitive_closure(cbind(x$bottom_item, x$top_item))), prefs, unique(df$assessor), SIMPLIFY = FALSE) } else { @@ -66,7 +66,7 @@ generate_transitive_closure <- function(df, cl = NULL) { "bottom_item" = "top_item", "top_item" = "bottom_item")) - if(nrow(check) > 0) { + if (nrow(check) > 0) { print("Inconsistent rankings:") print(check) stop("Cannot compute transitive closure. Please run compute_mallows with error_model='bernoulli'.") diff --git a/R/lik_db_mix.R b/R/lik_db_mix.R index 1b5eb976..3587f221 100644 --- a/R/lik_db_mix.R +++ b/R/lik_db_mix.R @@ -33,21 +33,21 @@ lik_db_mix <- function(rho, alpha, weights, metric, rankings, obs_freq = NULL, log = FALSE) { - if(!is.matrix(rankings)) { + if (!is.matrix(rankings)) { rankings <- matrix(rankings, nrow = 1) } - if(!is.null(obs_freq)) { - if(nrow(rankings) != length(obs_freq)) { + if (!is.null(obs_freq)) { + if (nrow(rankings) != length(obs_freq)) { stop("obs_freq must be of same length as the number of rows in rankings") } } - if(!is.matrix(rho)) { + if (!is.matrix(rho)) { rho <- matrix(rho, nrow = 1) } - if(is.null(obs_freq)) { + if (is.null(obs_freq)) { obs_freq <- rep(1, nrow(rankings)) } @@ -63,7 +63,7 @@ lik_db_mix <- function(rho, alpha, weights, metric, rankings = rankings, obs_freq = obs_freq) - if(!log) out <- exp(out) + if (!log) out <- exp(out) return(out) } diff --git a/R/misc.R b/R/misc.R index 584adeac..a617fa47 100644 --- a/R/misc.R +++ b/R/misc.R @@ -13,9 +13,9 @@ NULL #' @return TRUE if vec is a permutation #' @keywords internal validate_permutation <- function(vec) { - if(!any(is.na(vec))) { + if (!any(is.na(vec))) { return(all(sort(vec) == seq_along(vec))) - } else if(all(is.na(vec))) { + } else if (all(is.na(vec))) { return(TRUE) } else { return(all(vec[!is.na(vec)] <= length(vec)) && @@ -35,14 +35,14 @@ scalefun <- function(x) sprintf("%d", as.integer(x)) prepare_partition_function <- function(logz_estimate, metric, n_items) { # First, has the user supplied an estimate? - if(!is.null(logz_estimate)) { + if (!is.null(logz_estimate)) { return(list(cardinalities = NULL, logz_estimate = logz_estimate)) } # Second, do we have a sequence? relevant_params <- dplyr::filter(partition_function_data, .data$n_items == !!n_items, .data$metric == !!metric, .data$type == "cardinalities") - if(nrow(relevant_params) == 1) { + if (nrow(relevant_params) == 1) { return(list(cardinalities = unlist(relevant_params$values), logz_estimate = NULL)) } @@ -50,12 +50,12 @@ prepare_partition_function <- function(logz_estimate, metric, n_items) { relevant_params <- dplyr::filter(partition_function_data, .data$n_items == !!n_items, .data$metric == !!metric, .data$type == "importance_sampling") - if(nrow(relevant_params) == 1) { + if (nrow(relevant_params) == 1) { return(list(cardinalities = NULL, logz_estimate = unlist(relevant_params$values))) } # Fourth, is it the Ulam distance? - if(metric == "ulam") { + if (metric == "ulam") { return(list( cardinalities = unlist(lapply(0:(n_items - 1), function(x) PerMallows::count.perms(perm.length = n_items, dist.value = x, dist.name = "ulam"))) @@ -63,7 +63,7 @@ prepare_partition_function <- function(logz_estimate, metric, n_items) { } # Fifth, can we compute the partition function in our C++ code? - if(metric %in% c("cayley", "hamming", "kendall")) { + if (metric %in% c("cayley", "hamming", "kendall")) { return(list(cardinalities = NULL, logz_estimate = NULL)) } @@ -110,13 +110,13 @@ fill_single_entries <- function(data) { ## Source: https://stackoverflow.com/questions/11095992/generating-all-distinct-permutations-of-a-list-in-r permutations <- function(n) { - if(n == 1) { + if (n == 1) { return(matrix(1)) } else { sp <- permutations(n - 1) p <- nrow(sp) A <- matrix(nrow = n * p, ncol = n) - for(i in 1:n) { + for (i in 1:n) { A[(i - 1) * p + 1:p, ] <- cbind(i, sp + (sp >= i)) } return(A) diff --git a/R/misc_expected_dist.R b/R/misc_expected_dist.R index 0a6e813b..e2667ab2 100644 --- a/R/misc_expected_dist.R +++ b/R/misc_expected_dist.R @@ -6,12 +6,12 @@ #/' @return Expected value of the Kendall metric under the Mallows rank model with the Kendall distance. exp_d_tau <- function(alpha, n_items) { - if(alpha > 0) { + if (alpha > 0) { idx <- seq(from = 1, to = n_items, by = 1) out <- n_items * exp(-alpha) / (1 - exp(-alpha)) - sum((idx * exp(-idx * alpha)) / (1 - exp(-idx * alpha))) } else { - if(alpha == 0) { + if (alpha == 0) { out <- n_items * (n_items - 1) / 4 } else { stop("alpha must be a non-negative value") @@ -43,7 +43,7 @@ exp_d_cay <- function(alpha, n_items) { exp_d_ham <- function(alpha, n_items) { idx <- seq(from = 0, to = n_items, by = 1) out <- n_items - exp(alpha) * - sum(((exp(alpha) - 1)^idx[-(n_items + 1)]) / base::factorial(idx[-(n_items + 1)])) / + sum(((exp(alpha) - 1)^idx[- (n_items + 1)]) / base::factorial(idx[- (n_items + 1)])) / sum(((exp(alpha) - 1)^idx) / base::factorial(idx)) return(out) } diff --git a/R/misc_likelihood.R b/R/misc_likelihood.R index 8c14e8e7..d1d04216 100644 --- a/R/misc_likelihood.R +++ b/R/misc_likelihood.R @@ -16,8 +16,8 @@ log_lik_db <- function(rho, alpha, metric, rankings, obs_freq) { N <- sum(obs_freq) n_items <- ncol(rankings) - if(metric %in% c("kendall", "cayley", "hamming")) { - log_lik <- -( + if (metric %in% c("kendall", "cayley", "hamming")) { + log_lik <- - ( alpha * rank_dist_sum( rankings = t(rankings), rho = rho, metric = metric, obs_freq = obs_freq @@ -29,20 +29,20 @@ log_lik_db <- function(rho, alpha, metric, rankings, obs_freq) { ) } - if(metric %in% c( "ulam", "footrule", "spearman")) { + if (metric %in% c("ulam", "footrule", "spearman")) { pfd <- dplyr::filter(partition_function_data, .data$metric == !!metric, .data$n_items == !!n_items, .data$type == "cardinalities") - if(nrow(pfd) == 0) { + if (nrow(pfd) == 0) { stop("Given number of items currently not available for the specified metric") } else{ card <- pfd$values[[1]] } - log_lik <- -( + log_lik <- - ( alpha * rank_dist_sum(rankings = t(rankings), rho = rho, metric = metric, obs_freq = obs_freq) + - N * get_partition_function( alpha = alpha * n_items, + N * get_partition_function(alpha = alpha * n_items, n_items = n_items, metric = metric, cardinalities = card)) #TODO #91: write this part in particular as new function? } @@ -76,11 +76,11 @@ log_lik_db_mix <- function(rho, alpha, weights, metric, L <- length(obs_freq) n_clusters <- length(weights) temp <- matrix(NA, nrow = n_clusters, ncol = L) - for(l in seq_len(L)) { - for(g in seq_len(n_clusters)) { + for (l in seq_len(L)) { + for (g in seq_len(n_clusters)) { temp[g, l] <- exp(log_lik_db( rho = rho[g, ], alpha = alpha[g], metric = metric, - rankings = rankings[ l, , drop = FALSE], + rankings = rankings[l, , drop = FALSE], obs_freq = obs_freq[l])) } } diff --git a/R/plot.BayesMallows.R b/R/plot.BayesMallows.R index ff6ba50a..b1091a20 100644 --- a/R/plot.BayesMallows.R +++ b/R/plot.BayesMallows.R @@ -29,14 +29,14 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = # issue a warning. This is because plot.BayesMallows must have the same # required arguments as graphics::plot. - if(is.null(burnin)) { + if (is.null(burnin)) { stop("Please specify the burnin.") } - if(x$nmc <= burnin) stop("burnin must be <= nmc") + if (x$nmc <= burnin) stop("burnin must be <= nmc") stopifnot(parameter %in% c("alpha", "rho", "cluster_probs", "cluster_assignment", "theta")) - if(parameter == "alpha") { + if (parameter == "alpha") { df <- dplyr::filter(x$alpha, .data$iteration > burnin) p <- ggplot2::ggplot(df, ggplot2::aes(x = .data$value)) + @@ -44,22 +44,22 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = ggplot2::xlab(expression(alpha)) + ggplot2::ylab("Posterior density") - if(x$n_clusters > 1) { + if (x$n_clusters > 1) { p <- p + ggplot2::facet_wrap(~ .data$cluster, scales = "free_x") } return(p) - } else if(parameter == "rho") { + } else if (parameter == "rho") { - if(is.null(items) && x$n_items > 5) { + if (is.null(items) && x$n_items > 5) { message("Items not provided by user. Picking 5 at random.") items <- sample.int(x$n_items, 5) } else if (is.null(items) && x$n_items > 0) { items <- seq.int(from = 1, to = x$n_items) } - if(!is.character(items)) { + if (!is.character(items)) { items <- x$items[items] } @@ -78,14 +78,14 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = ggplot2::xlab("rank") + ggplot2::ylab("Posterior probability") - if(x$n_clusters == 1) { + if (x$n_clusters == 1) { p <- p + ggplot2::facet_wrap(~ .data$item) } else { p <- p + ggplot2::facet_wrap(~ .data$cluster + .data$item) } return(p) - } else if(parameter == "cluster_probs") { + } else if (parameter == "cluster_probs") { df <- dplyr::filter(x$cluster_probs, .data$iteration > burnin) ggplot2::ggplot(df, ggplot2::aes(x = .data$value)) + @@ -94,9 +94,9 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = ggplot2::ylab("Posterior density") + ggplot2::facet_wrap(~ .data$cluster) - } else if(parameter == "cluster_assignment") { + } else if (parameter == "cluster_assignment") { - if(is.null(x$cluster_assignment)) { + if (is.null(x$cluster_assignment)) { stop("Please rerun compute_mallows with save_clus = TRUE") } @@ -121,9 +121,9 @@ plot.BayesMallows <- function(x, burnin = x$burnin, parameter = "alpha", items = ) + ggplot2::xlab(paste0("Assessors (", min(assessor_order), " - ", max(assessor_order), ")")) - } else if(parameter == "theta") { + } else if (parameter == "theta") { - if(is.null(x$theta)) { + if (is.null(x$theta)) { stop("Please run compute_mallows with error_model = 'bernoulli'.") } diff --git a/R/plot_elbow.R b/R/plot_elbow.R index 5809234d..1da27321 100644 --- a/R/plot_elbow.R +++ b/R/plot_elbow.R @@ -29,26 +29,26 @@ plot_elbow <- function(..., burnin = NULL) { models <- list(...) # Taking into account the case where the user has entered a list of models - if(length(models) == 1 && !(class(models[[1]]) == "BayesMallows")) { + if (length(models) == 1 && !(class(models[[1]]) == "BayesMallows")) { models <- models[[1]] } df <- do.call(rbind, lapply(models, function(x) { stopifnot(class(x) == "BayesMallows") - if(!("burnin" %in% names(x))) { - if(is.null(burnin)) { + if (!("burnin" %in% names(x))) { + if (is.null(burnin)) { stop("burnin not provided") } else { x$burnin <- burnin } } - if(!x$include_wcd) stop("To get an elbow plot, set include_wcd=TRUE in compute_mallows") + if (!x$include_wcd) stop("To get an elbow plot, set include_wcd=TRUE in compute_mallows") df <- dplyr::filter(x$within_cluster_distance, .data$iteration > x$burnin) - if(nrow(df) <= 0) stop("burnin must be strictly smaller than the number of MCMC samples") + if (nrow(df) <= 0) stop("burnin must be strictly smaller than the number of MCMC samples") # Need to sum the within-cluster distances across clusters, for each iteration df <- dplyr::group_by(df, .data$iteration) diff --git a/R/plot_top_k.R b/R/plot_top_k.R index a6952d8d..d2b94011 100644 --- a/R/plot_top_k.R +++ b/R/plot_top_k.R @@ -48,7 +48,7 @@ plot_top_k <- function(model_fit, burnin = model_fit$burnin, # Sort the items according to probability in Cluster 1 item_ordering <- compute_consensus(model_fit, type = "CP", burnin = burnin) - if(model_fit$n_clusters > 1) { + if (model_fit$n_clusters > 1) { item_ordering <- rev(item_ordering[item_ordering$cluster == "Cluster 1", ]$item) } else { item_ordering <- rev(item_ordering$item) @@ -57,7 +57,7 @@ plot_top_k <- function(model_fit, burnin = model_fit$burnin, rho <- dplyr::mutate(rho, item = factor(.data$item, levels = unique(item_ordering))) # Trick to make the plot look nicer - if(model_fit$n_clusters == 1) { + if (model_fit$n_clusters == 1) { rho <- dplyr::mutate(rho, cluster = "") } @@ -82,7 +82,7 @@ plot_top_k <- function(model_fit, burnin = model_fit$burnin, ggplot2::xlab(expression(rho)) + ggplot2::theme(legend.position = "none") - if(model_fit$n_clusters > 1) { + if (model_fit$n_clusters > 1) { rho_plot <- rho_plot + ggplot2::facet_wrap(~ .data$cluster) } diff --git a/R/predict_top_k.R b/R/predict_top_k.R index 746861ee..37f6a196 100644 --- a/R/predict_top_k.R +++ b/R/predict_top_k.R @@ -51,12 +51,12 @@ predict_top_k <- function(model_fit, burnin = model_fit$burnin, validate_top_k <- function(model_fit, burnin) { - if(is.null(burnin)) { + if (is.null(burnin)) { stop("Please specify the burnin.") } stopifnot(burnin < model_fit$nmc) - if(!exists("augmented_data", model_fit)) { + if (!exists("augmented_data", model_fit)) { stop("model_fit must have element augmented_data. Please set save_aug = TRUE in compute_mallows in order to create a top-k plot.") } diff --git a/R/print.BayesMallows.R b/R/print.BayesMallows.R index 1eac4632..a288c90e 100644 --- a/R/print.BayesMallows.R +++ b/R/print.BayesMallows.R @@ -16,7 +16,7 @@ print.BayesMallows <- function(x, ...) { # issue a warning. This is because print.BayesMallows must have the same # required arguments as base::print. - if(is.null(x$n_items) || is.null(x$n_assessors)) { + if (is.null(x$n_items) || is.null(x$n_assessors)) { stop("BayesMallows object must have elements n_items and n_assessors.") } cat("Bayesian Mallows Model with", x$n_items, "items and", x$n_assessors, "assessors.\n") diff --git a/R/print.BayesMallowsMixtures.R b/R/print.BayesMallowsMixtures.R index 6bfd95aa..3749b24d 100644 --- a/R/print.BayesMallowsMixtures.R +++ b/R/print.BayesMallowsMixtures.R @@ -16,7 +16,7 @@ print.BayesMallowsMixtures <- function(x, ...) { # issue a warning. This is because print.BayesMallowsMixtures must have the same # required arguments as base::print. - if(!Reduce(`&`, lapply(x, function(x) inherits(x, "BayesMallows")))) { + if (!Reduce(`&`, lapply(x, function(x) inherits(x, "BayesMallows")))) { stop("All elements of a BayesMallowsMixtures object must be of class BayesMallows.") } diff --git a/R/rank_conversion.R b/R/rank_conversion.R index 5d11e150..05f28e88 100644 --- a/R/rank_conversion.R +++ b/R/rank_conversion.R @@ -45,10 +45,10 @@ NULL create_ranking <- function(orderings) { # Check that it is a permutation - if(is.vector(orderings)) { + if (is.vector(orderings)) { stopifnot(validate_permutation(orderings)) return(order(orderings)) - } else if(is.matrix(orderings)) { + } else if (is.matrix(orderings)) { n_items <- ncol(orderings) # Convert to list, for easier functional programming @@ -57,7 +57,7 @@ create_ranking <- function(orderings) { # Check that matrix contains permutations check <- lapply(orderings, validate_permutation) - if(!Reduce(`&&`, check)) { + if (!Reduce(`&&`, check)) { stop(paste("orderings must contain proper permutations. Problem row(s):", which(!check))) } @@ -74,7 +74,7 @@ create_ranking <- function(orderings) { inds[is.na(inds)] <- FALSE # Extract the correct items candidates[inds] - } ) + }) return(t(matrix(unlist(rankings), ncol = length(rankings)))) } else { diff --git a/R/rank_distance.R b/R/rank_distance.R index bb778d02..e0ec36be 100644 --- a/R/rank_distance.R +++ b/R/rank_distance.R @@ -22,13 +22,13 @@ #' @example /inst/examples/rank_distance_example.R rank_distance <- function(rankings, rho, metric, obs_freq = 1) { - if(!is.matrix(rankings)) { + if (!is.matrix(rankings)) { rankings <- matrix(rankings, nrow = 1) } stopifnot(length(obs_freq) == 1 || length(obs_freq) == nrow(rankings)) - if(length(obs_freq) == 1) { + if (length(obs_freq) == 1) { obs_freq <- rep(obs_freq, nrow(rankings)) } diff --git a/R/rank_freq_distr.R b/R/rank_freq_distr.R index 32e736f5..86911f92 100644 --- a/R/rank_freq_distr.R +++ b/R/rank_freq_distr.R @@ -15,7 +15,7 @@ #' rank_freq_distr <- function(rankings) { - if(!is.matrix(rankings)) { + if (!is.matrix(rankings)) { rankings <- matrix(rankings, nrow = 1) } diff --git a/R/sample_mallows.R b/R/sample_mallows.R index cf895a7b..557956e8 100644 --- a/R/sample_mallows.R +++ b/R/sample_mallows.R @@ -51,13 +51,13 @@ sample_mallows <- function(rho0, alpha0, n_samples, max_lag = 1000L) { - if(!(validate_permutation(rho0) && sum(is.na(rho0)) == 0)) { + if (!(validate_permutation(rho0) && sum(is.na(rho0)) == 0)) { stop("rho0 must be a proper ranking with no missing values.") } - if(diagnostic && n_samples == 1) { + if (diagnostic && n_samples == 1) { stop("Must have more than one samples to create diagnostic plots") - } else if(n_samples <= 0) { + } else if (n_samples <= 0) { stop("n_samples must be positive.") } @@ -84,8 +84,8 @@ sample_mallows <- function(rho0, alpha0, n_samples, obs_freq = rep(1, internal_n_samples) )) - if(diagnostic) { - if(is.null(items_to_plot) && n_items > 5) { + if (diagnostic) { + if (is.null(items_to_plot) && n_items > 5) { message("Items not provided by user. Picking 5 at random.") items_to_plot <- sample.int(n_items, 5) } else { diff --git a/R/smc_post_processing_functions.R b/R/smc_post_processing_functions.R index 24f0c174..7bbc9a93 100644 --- a/R/smc_post_processing_functions.R +++ b/R/smc_post_processing_functions.R @@ -11,7 +11,7 @@ smc_processing <- function(output, colnames = NULL) { df <- data.frame(data = output) # if colnames are specified, then incorporate them - if(is.null(colnames)) { + if (is.null(colnames)) { n_items <- ncol(df) cletters <- rep(c("Item"), times = n_items) cindexes <- (c(1:n_items)) @@ -71,7 +71,7 @@ compute_posterior_intervals_rho <- function(output, nmc, burnin, colnames = NULL } #------------------------------------------------------------------------------------------ - if(verbose) print(rho_posterior_interval) + if (verbose) print(rho_posterior_interval) return(rho_posterior_interval) } @@ -176,7 +176,7 @@ plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = n_items <- dim(output)[2] - if(is.null(items) && n_items > 5) { + if (is.null(items) && n_items > 5) { message("Items not provided by user or more than 5 items in a ranking. Picking 5 at random.") items <- sample(1:n_items, 5, replace = F) items <- sort(items) @@ -189,14 +189,14 @@ plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = # do smc processing here smc_plot <- smc_processing(output = output, colnames = colnames) - if(!is.character(items)) { + if (!is.character(items)) { items <- unique(smc_plot$item)[items] } iteration <- rep(c(1:nmc), times = n_items) df <- cbind(iteration, smc_plot) - if(C == 1) { + if (C == 1) { df <- cbind(cluster = "Cluster 1", df) } @@ -220,7 +220,7 @@ plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = ggplot2::xlab("rank") + ggplot2::ylab("Posterior probability") - if(C == 1) { + if (C == 1) { p <- p + ggplot2::facet_wrap(~ .data$item) } else { p <- p + ggplot2::facet_wrap(~ .data$cluster + .data$item) diff --git a/R/tidy_mcmc.R b/R/tidy_mcmc.R index 376a474b..b08d8497 100644 --- a/R/tidy_mcmc.R +++ b/R/tidy_mcmc.R @@ -79,8 +79,8 @@ tidy_alpha <- function(fit) { tidy_cluster_assignment <- function(fit) { # Tidy cluster assignment - if(fit$save_clus) { - if(fit$n_clusters > 1) { + if (fit$save_clus) { + if (fit$n_clusters > 1) { cluster_dims <- dim(fit$cluster_assignment) value <- paste("Cluster", c(fit$cluster_assignment)) } else { @@ -116,7 +116,7 @@ tidy_cluster_assignment <- function(fit) { tidy_cluster_probabilities <- function(fit) { # Tidy cluster probabilities - if(fit$n_clusters > 1) { + if (fit$n_clusters > 1) { clusprob_dims <- dim(fit$cluster_probs) value <- c(fit$cluster_probs) } else { @@ -151,7 +151,7 @@ tidy_cluster_probabilities <- function(fit) { tidy_wcd <- function(fit) { # Tidy the within-cluster distances, or delete the empty matrix - if(fit$include_wcd) { + if (fit$include_wcd) { wcd_dims <- dim(fit$within_cluster_distance) value <- c(fit$within_cluster_distance) @@ -185,7 +185,7 @@ tidy_wcd <- function(fit) { tidy_augmented_data <- function(fit) { # Tidy augmented data, or delete - if(fit$save_aug) { + if (fit$save_aug) { augdata_dims <- dim(fit$augmented_data) @@ -220,7 +220,7 @@ tidy_augmented_data <- function(fit) { tidy_augmentation_acceptance <- function(fit) { # Augmentation acceptance - if(fit$any_missing || fit$augpair) { + if (fit$any_missing || fit$augpair) { fit$aug_acceptance <- dplyr::tibble(acceptance_rate = c(fit$aug_acceptance)) fit$aug_acceptance <- dplyr::mutate(fit$aug_acceptance, assessor = dplyr::row_number()) @@ -238,7 +238,7 @@ tidy_augmentation_acceptance <- function(fit) { tidy_error_probability <- function(fit) { theta_length <- length(fit$theta) - if(theta_length > 0) { + if (theta_length > 0) { fit$theta <- dplyr::tibble( iteration = seq(from = 1, to = theta_length, by = 1), value = c(fit$theta) diff --git a/tests/testthat/test-compute_mallows.R b/tests/testthat/test-compute_mallows.R index cb8eb3c7..d5bf223a 100644 --- a/tests/testthat/test-compute_mallows.R +++ b/tests/testthat/test-compute_mallows.R @@ -66,7 +66,7 @@ test_that("compute_mallows with missing data works", { test_that("compute_mallows runs with the right distances", { - for(metric in c("footrule", "spearman", "cayley", "kendall", "ulam", "hamming")) { + for (metric in c("footrule", "spearman", "cayley", "kendall", "ulam", "hamming")) { expect_s3_class(compute_mallows(potato_visual, metric = metric, nmc = 3), "BayesMallows") } diff --git a/tests/testthat/test-distance_function.R b/tests/testthat/test-distance_function.R index 4737f326..66cd0873 100644 --- a/tests/testthat/test-distance_function.R +++ b/tests/testthat/test-distance_function.R @@ -9,7 +9,7 @@ check_dist <- function(n, fun) { # Loop over some n values test_that("footrule distance is correct", { - for(n in c(2, 3, 5)) { + for (n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "footrule") @@ -20,7 +20,7 @@ test_that("footrule distance is correct", { # Loop over some n values test_that("Spearman distance is correct", { - for(n in c(2, 3, 5)) { + for (n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "spearman") @@ -31,7 +31,7 @@ test_that("Spearman distance is correct", { # Loop over some n values test_that("Kendall distance is correct", { - for(n in c(2, 3, 5)) { + for (n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "kendall") @@ -44,7 +44,7 @@ test_that("Kendall distance is correct", { # Loop over some n values test_that("Cayley distance is correct", { - for(n in c(2, 3, 5)) { + for (n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "cayley") @@ -57,7 +57,7 @@ test_that("Cayley distance is correct", { # Loop over some n values test_that("Hamming distance is correct", { - for(n in c(2, 3, 5)) { + for (n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "hamming") @@ -70,7 +70,7 @@ test_that("Hamming distance is correct", { # Loop over some n values test_that("Ulam distance is correct", { - for(n in c(2, 3, 5)) { + for (n in c(2, 3, 5)) { expect_equal( check_dist(n, fun = function(r1, r2) { get_rank_distance(r1, r2, "ulam") diff --git a/tests/testthat/test-estimate_partition_function.R b/tests/testthat/test-estimate_partition_function.R index c73f5d43..accccda8 100644 --- a/tests/testthat/test-estimate_partition_function.R +++ b/tests/testthat/test-estimate_partition_function.R @@ -4,7 +4,7 @@ context("Testing function estimate_partition_function") test_that( "estimate_partition_function fails for wrong asymptotic metrics", { - for(metric in c("cayley", "hamming", "kendall", "ulam")) { + for (metric in c("cayley", "hamming", "kendall", "ulam")) { expect_error( estimate_partition_function(method = "asymptotic", alpha_vector = seq(from = 1, to = 2, by = .1), diff --git a/tests/testthat/test-misc_cpp.R b/tests/testthat/test-misc_cpp.R index f44870b2..eb37682d 100644 --- a/tests/testthat/test-misc_cpp.R +++ b/tests/testthat/test-misc_cpp.R @@ -31,7 +31,7 @@ test_that( n <- 10000L probs <- c(0.1, 0.2, 0.7) values <- integer(n) - for(i in seq(1L, n, 1L)) { + for (i in seq(1L, n, 1L)) { values[[i]] <- BayesMallows:::sample_int(probs) } freqs <- table(values) / length(values) @@ -42,7 +42,7 @@ test_that( # TRUE, due to the randomness in sampling skip_on_cran() diff <- abs(probs - freqs) - for(i in 1:3) { + for (i in 1:3) { expect_lt(diff[[i]], 0.02) } diff --git a/tests/testthat/test-partition_function.R b/tests/testthat/test-partition_function.R index 8a57dd8e..d6b7f715 100644 --- a/tests/testthat/test-partition_function.R +++ b/tests/testthat/test-partition_function.R @@ -8,23 +8,23 @@ check_log_zn <- function(n, alpha, metric) { # Compute the partition function - if(metric == "footrule") { - log(sum(exp(- alpha / n * colSums( abs(t(perm ) - 1:n ))))) - } else if(metric == "spearman") { - log(sum(exp(- alpha / n * colSums( (t(perm ) - 1:n )^2)))) - } else if(metric == "kendall") { + if (metric == "footrule") { + log(sum(exp(- alpha / n * colSums(abs(t(perm) - 1:n))))) + } else if (metric == "spearman") { + log(sum(exp(- alpha / n * colSums((t(perm) - 1:n)^2)))) + } else if (metric == "kendall") { log(sum(exp(- alpha / n * apply(perm, 1, get_rank_distance, r2 = 1:n, metric = "kendall")))) - } else if(metric == "cayley") { + } else if (metric == "cayley") { log(sum(exp(- alpha / n * apply(perm, 1, get_rank_distance, r2 = 1:n, metric = "cayley")))) - } else if(metric == "hamming") { + } else if (metric == "hamming") { log(sum(exp(- alpha / n * apply(perm, 1, get_rank_distance, r2 = 1:n, metric = "hamming")))) - } else if(metric == "ulam") { + } else if (metric == "ulam") { log(sum(unlist(lapply(seq(0, n - 1, by = 1), function(x) { PerMallows::count.perms(perm.length = n, dist.value = x, dist.name = "ulam") * exp(-alpha / n * x) })))) @@ -39,8 +39,8 @@ test_that("footrule partition function is correct", { footrule_sequence <- dplyr::filter(BayesMallows:::partition_function_data, metric == "footrule", type == "cardinalities")$values - for(n in c(1, 2, 3, 5)) { - for(alpha in c(0.001, 0.1, 1)) { + for (n in c(1, 2, 3, 5)) { + for (alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, cardinalities = footrule_sequence[[n]], metric = "footrule"), @@ -54,8 +54,8 @@ test_that("Spearman partition function is correct", { spearman_sequence <- dplyr::filter(BayesMallows:::partition_function_data, metric == "spearman", type == "cardinalities")$values - for(n in c(1, 2, 3)) { - for(alpha in c(0.001, 0.1, 1)) { + for (n in c(1, 2, 3)) { + for (alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, cardinalities = spearman_sequence[[n]], metric = "spearman"), @@ -66,8 +66,8 @@ test_that("Spearman partition function is correct", { test_that("Kendall partition function is correct", { - for(n in c(1, 2, 3)) { - for(alpha in c(0.001, 0.1, 1)) { + for (n in c(1, 2, 3)) { + for (alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, metric = "kendall"), check_log_zn(n, alpha, "kendall") @@ -76,8 +76,8 @@ test_that("Kendall partition function is correct", { }}) test_that("Cayley partition function is correct", { - for(n in c(1, 2, 3)) { - for(alpha in c(0.001, 0.1, 1)) { + for (n in c(1, 2, 3)) { + for (alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, metric = "cayley"), check_log_zn(n, alpha, "cayley") @@ -87,8 +87,8 @@ test_that("Cayley partition function is correct", { test_that("Hamming partition function is correct", { - for(n in c(1, 2, 3)) { - for(alpha in c(0.001, 0.1, 1)) { + for (n in c(1, 2, 3)) { + for (alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, metric = "hamming"), check_log_zn(n, alpha, "hamming") @@ -100,8 +100,8 @@ test_that("Ulam partition function is correct", { ulam_sequence <- dplyr::filter(BayesMallows:::partition_function_data, metric == "ulam", type == "cardinalities")$values - for(n in c(1, 2, 3)) { - for(alpha in c(0.001, 0.1, 1)) { + for (n in c(1, 2, 3)) { + for (alpha in c(0.001, 0.1, 1)) { expect_equal( get_partition_function(n = n, alpha = alpha, cardinalities = ulam_sequence[[n]], diff --git a/tests/testthat/test-sample_mallows.R b/tests/testthat/test-sample_mallows.R index 53e81538..2060e38e 100644 --- a/tests/testthat/test-sample_mallows.R +++ b/tests/testthat/test-sample_mallows.R @@ -8,7 +8,7 @@ rho0 <- seq(from = 1, to = n_items, by = 1) # Set the scale alpha0 <- 10 -for(m in c("footrule", "spearman", "cayley", "hamming", "kendall", "ulam")) { +for (m in c("footrule", "spearman", "cayley", "hamming", "kendall", "ulam")) { samples <- sample_mallows(rho0 = rho0, alpha0 = alpha0, n_samples = 100, burnin = 1000, thinning = 1000, metric = m, leap_size = 1) test_that( From 59dd9a4bc7ca207e7665724f678c61aeebb8395d Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 13:44:26 +0100 Subject: [PATCH 39/41] Replaced F with FALSE --- R/smc_post_processing_functions.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/smc_post_processing_functions.R b/R/smc_post_processing_functions.R index 7bbc9a93..7ea475a7 100644 --- a/R/smc_post_processing_functions.R +++ b/R/smc_post_processing_functions.R @@ -178,7 +178,7 @@ plot_rho_posterior <- function(output, nmc, burnin, C, colnames = NULL, items = if (is.null(items) && n_items > 5) { message("Items not provided by user or more than 5 items in a ranking. Picking 5 at random.") - items <- sample(1:n_items, 5, replace = F) + items <- sample(1:n_items, 5, replace = FALSE) items <- sort(items) } else if (is.null(items) && n_items <= 5) { From be5cf49e95e2dd62a24ac7daf27a5bfb450826ee Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 13:45:49 +0100 Subject: [PATCH 40/41] Removed superfluous trailing blank lines --- R/RcppExports.R | 1 - tests/testthat.R | 1 - tests/testthat/test-mcmc_function.R | 1 - 3 files changed, 3 deletions(-) diff --git a/R/RcppExports.R b/R/RcppExports.R index 97204410..df5717cb 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -649,4 +649,3 @@ metropolis_hastings_aug_ranking_pseudo <- function(alpha, rho, n_items, partial_ metropolis_hastings_rho <- function(alpha, n_items, rankings, metric, rho, leap_size) { .Call(`_BayesMallows_metropolis_hastings_rho`, alpha, n_items, rankings, metric, rho, leap_size) } - diff --git a/tests/testthat.R b/tests/testthat.R index c8594a2a..06eb8dc1 100644 --- a/tests/testthat.R +++ b/tests/testthat.R @@ -1,4 +1,3 @@ library(testthat) library(BayesMallows) test_check("BayesMallows") - diff --git a/tests/testthat/test-mcmc_function.R b/tests/testthat/test-mcmc_function.R index 0d21112a..780bae13 100644 --- a/tests/testthat/test-mcmc_function.R +++ b/tests/testthat/test-mcmc_function.R @@ -38,4 +38,3 @@ test_that( nrow() == 0 ) ) - From de55d2a30fc376b61890d72f59dc723a80a5db2b Mon Sep 17 00:00:00 2001 From: Waldir Leoncio Date: Mon, 6 Dec 2021 13:47:57 +0100 Subject: [PATCH 41/41] Removed unnecessary concatenation --- R/smc_post_processing_functions.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/smc_post_processing_functions.R b/R/smc_post_processing_functions.R index 7ea475a7..07280934 100644 --- a/R/smc_post_processing_functions.R +++ b/R/smc_post_processing_functions.R @@ -13,7 +13,7 @@ smc_processing <- function(output, colnames = NULL) { # if colnames are specified, then incorporate them if (is.null(colnames)) { n_items <- ncol(df) - cletters <- rep(c("Item"), times = n_items) + cletters <- rep("Item", times = n_items) cindexes <- (c(1:n_items)) cnames <- c(paste(cletters, cindexes, sep = " ")) colnames(df) <- cnames

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zmHtMu5==mKn7NqriNSw*r1|vUlP^NH3^mM6Jyht7@xSR5@{e^b&z5a9_@^XZ6bPEl*3q7wAlQrN{qj dXdRvO9h}_0T}U_p8xY9+?JiMLiphz?{XbMkGFSir diff --git a/vignettes/SMC-Mallows.Rmd b/vignettes/SMC-Mallows.Rmd index 97fa8568..818a81c7 100644 --- a/vignettes/SMC-Mallows.Rmd +++ b/vignettes/SMC-Mallows.Rmd @@ -15,7 +15,7 @@ vignette: > knitr::opts_chunk$set(echo = TRUE) ``` -This vignette describes the extension to the `BayesMallows` R package called `SMC-Mallows`. This uses Sequential Monte Carlo (SMC) algorithms to provide updated approximations to the posterior distribution of a single Mallows model. We consider scenarios where we receive sequential information in the form of complete rankings, partial rankings and updated rankings from existing individuals who have previously provided a (partial) ranking. We use an alternative data augmentation method, called the pseudolikelihood approach, when we are using the footrule and Spearman distance functions instead of using an independent sampler. The extension currently uses functions, similiar to the `BayesMallows` R package, to visualise and analyse the posterior distributions. +This vignette describes the extension to the `BayesMallows` R package called `SMC-Mallows`. This uses Sequential Monte Carlo (SMC) algorithms to provide updated approximations to the posterior distribution of a single Mallows model. We consider scenarios where we receive sequential information in the form of complete rankings, partial rankings and updated rankings from existing individuals who have previously provided a (partial) ranking. We use an alternative data augmentation method, called the pseudolikelihood approach, when we are using the footrule and Spearman distance functions instead of using an independent sampler. The extension currently uses functions, similar to the `BayesMallows` R package, to visualise and analyse the posterior distributions. ## Overview of Extension @@ -271,7 +271,7 @@ smc_partial_test <- smc_mallows_new_users_partial( ) ``` -The variable `smc_test_partial` contains a list with three dimensional matrix of size `N` by `n_items` by `Time+1`, named `rho_samples`, and a `N` by `Time+1` matrix called `alpha_samples`. The analysis performed in the previous demonstration can be applied to this scenario. Here, we can observe the posterior porbabilities for a selection of items in $\boldsymbol{\rho}$ and posterior density for $\alpha$ as part of this demonstration, but we can use other post processing functions to analyse the output. +The variable `smc_test_partial` contains a list with three dimensional matrix of size `N` by `n_items` by `Time+1`, named `rho_samples`, and a `N` by `Time+1` matrix called `alpha_samples`. The analysis performed in the previous demonstration can be applied to this scenario. Here, we can observe the posterior probabilities for a selection of items in $\boldsymbol{\rho}$ and posterior density for $\alpha$ as part of this demonstration, but we can use other post processing functions to analyse the output. ```{r smc_partial_analysis, message=FALSE, warning=FALSE} partial_test_sample_rho <- smc_partial_test$rho_samples[, , Time + 1] From 87697eea9e855f6ebc8e3fe68de45b92fe70bbae Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Mon, 22 Nov 2021 15:35:30 +0100 Subject: [PATCH 21/41] corrected more typos with devtools::spellcheck() --- R/RcppExports.R | 4 ++-- man/calculate_backward_probability.Rd | 2 +- man/correction_kernel.Rd | 2 +- src/smc_calculate_backward_probability.cpp | 2 +- src/smc_correction_kernel.cpp | 2 +- vignettes/SMC-Mallows.Rmd | 2 +- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/R/RcppExports.R b/R/RcppExports.R index 45d7a3e2..97204410 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -206,7 +206,7 @@ run_mcmc <- function(rankings, obs_freq, nmc, constraints, cardinalities, logz_e #' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and #' \code{"ulam"}. #' @return backward_auxiliary_ranking_probability A numerical value of creating the previous augmented ranking using the same item ordering used to create the -#' new auggmented ranking in calculate_forward_probability funtion. +#' new augmented ranking in calculate_forward_probability function. #' @export calculate_backward_probability <- function(item_ordering, partial_ranking, current_ranking, remaining_set, rho, alpha, n_items, metric) { .Call(`_BayesMallows_calculate_backward_probability`, item_ordering, partial_ranking, current_ranking, remaining_set, rho, alpha, n_items, metric) @@ -243,7 +243,7 @@ calculate_forward_probability <- function(item_ordering, partial_ranking, remain #' @title Correction Kernel #' @description Function to determine if the augmented ranking is compatible #' with the new observed partial ranking. If it is not, the we create a new -#' augmentation using the random sampling approachand calculate the +#' augmentation using the random sampling approach and calculate the #' augmentation probability. #' #' @param current_ranking A ranking sequence vector of the current augmented diff --git a/man/calculate_backward_probability.Rd b/man/calculate_backward_probability.Rd index 71ac908a..788ada7e 100644 --- a/man/calculate_backward_probability.Rd +++ b/man/calculate_backward_probability.Rd @@ -37,7 +37,7 @@ Bayesian Mallows Model. Available options are \code{"footrule"}, } \value{ backward_auxiliary_ranking_probability A numerical value of creating the previous augmented ranking using the same item ordering used to create the -new auggmented ranking in calculate_forward_probability funtion. +new augmented ranking in calculate_forward_probability function. } \description{ Function to calculate probability of assigning a set of specific ranks to an specific item diff --git a/man/correction_kernel.Rd b/man/correction_kernel.Rd index aeb489af..db06499e 100644 --- a/man/correction_kernel.Rd +++ b/man/correction_kernel.Rd @@ -23,6 +23,6 @@ that is compatible with the new observed ranking for a user \description{ Function to determine if the augmented ranking is compatible with the new observed partial ranking. If it is not, the we create a new -augmentation using the random sampling approachand calculate the +augmentation using the random sampling approach and calculate the augmentation probability. } diff --git a/src/smc_calculate_backward_probability.cpp b/src/smc_calculate_backward_probability.cpp index d8fd648d..979b9a00 100644 --- a/src/smc_calculate_backward_probability.cpp +++ b/src/smc_calculate_backward_probability.cpp @@ -19,7 +19,7 @@ //' \code{"spearman"}, \code{"cayley"}, \code{"hamming"}, \code{"kendall"}, and //' \code{"ulam"}. //' @return backward_auxiliary_ranking_probability A numerical value of creating the previous augmented ranking using the same item ordering used to create the -//' new auggmented ranking in calculate_forward_probability funtion. +//' new augmented ranking in calculate_forward_probability function. //' @export // [[Rcpp::export]] double calculate_backward_probability( diff --git a/src/smc_correction_kernel.cpp b/src/smc_correction_kernel.cpp index 98aee5eb..500f38a4 100644 --- a/src/smc_correction_kernel.cpp +++ b/src/smc_correction_kernel.cpp @@ -4,7 +4,7 @@ //' @title Correction Kernel //' @description Function to determine if the augmented ranking is compatible //' with the new observed partial ranking. If it is not, the we create a new -//' augmentation using the random sampling approachand calculate the +//' augmentation using the random sampling approach and calculate the //' augmentation probability. //' //' @param current_ranking A ranking sequence vector of the current augmented diff --git a/vignettes/SMC-Mallows.Rmd b/vignettes/SMC-Mallows.Rmd index 818a81c7..d4752e7f 100644 --- a/vignettes/SMC-Mallows.Rmd +++ b/vignettes/SMC-Mallows.Rmd @@ -55,7 +55,7 @@ We provide a summary on the Bayesian Mallows model and the proposed Sequential M ### Notation -We have set of $m$ distinct items, labelled $\mathcal{A} = \{A_1, \dots, A_m \}$, and we are asked to rank these items in order of personal preference with respect to some attribute. This information can be expressed as a ranking $\boldsymbol{R} = \{ R_1 , \dots , R_m\}$, which is a mapping $\boldsymbol{R}:\mathcal{A} \rightarrow \mathcal{P}_m$, where $\mathcal{P}_m$ is the space of $m$-dimensional permutations of $\{1, \dots, m\}$. Each $R_i \in \{1,\dots,m\}$ corresponds to the rank of an item $A_i$. We fundamentally assume that the preference information we receive from a group of individuals is transitive, i.e., each individual does not contradict themselves when specifying their preferences. In other words, for any three distinct items $\{A_i,A_j,A_k\}$ in a set, then if $A_i \prec A_j$ and $A_j \prec A_k$, then it must follow that $A_i \prec A_k$. Sometimes, we are unable to provide full rankings, so instead we provide a ranking for a subset of the items in $\mathcal{A}$. These are referred to as partial rankings. Partial rankings can occur either randomly or an individal can specify their top-k ranked items. In this scenario, we will need perform data augmentation in order to estimate the parameters of the Mallows model. +We have set of $m$ distinct items, labelled $\mathcal{A} = \{A_1, \dots, A_m \}$, and we are asked to rank these items in order of personal preference with respect to some attribute. This information can be expressed as a ranking $\boldsymbol{R} = \{ R_1 , \dots , R_m\}$, which is a mapping $\boldsymbol{R}:\mathcal{A} \rightarrow \mathcal{P}_m$, where $\mathcal{P}_m$ is the space of $m$-dimensional permutations of $\{1, \dots, m\}$. Each $R_i \in \{1,\dots,m\}$ corresponds to the rank of an item $A_i$. We fundamentally assume that the preference information we receive from a group of individuals is transitive, i.e., each individual does not contradict themselves when specifying their preferences. In other words, for any three distinct items $\{A_i,A_j,A_k\}$ in a set, then if $A_i \prec A_j$ and $A_j \prec A_k$, then it must follow that $A_i \prec A_k$. Sometimes, we are unable to provide full rankings, so instead we provide a ranking for a subset of the items in $\mathcal{A}$. These are referred to as partial rankings. Partial rankings can occur either randomly or an individual can specify their top-k ranked items. In this scenario, we will need perform data augmentation in order to estimate the parameters of the Mallows model. ### The Bayesian Mallows Model From 93b3702a7d41ec816a2cd78203555adc271ad1a0 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=C3=98ystein=20S=C3=B8rensen?= Date: Mon, 22 Nov 2021 16:01:08 +0100 Subject: [PATCH 22/41] accidentally deleted vignettes/BayesMallows.pdf in previous commit. fixed here --- vignettes/BayesMallows.pdf | Bin 0 -> 1545953 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 vignettes/BayesMallows.pdf diff --git a/vignettes/BayesMallows.pdf b/vignettes/BayesMallows.pdf new file mode 100644 index 0000000000000000000000000000000000000000..54da572052565b686364a92a458e2f9caefb2f4c GIT binary patch literal 1545953 zcma&NQ>-vd(5<;`+qP}nwr$(*wr$(CZQHhO@A=NmWRU;nbXGd+vO0BB^<*`vf`}L` zBONOg>HPA@Ius)_69EH(y^$3Z4-XW*jH#WuivhE}D>RT>ZYF697bJV%i@yeeD;#%L0J-0kltC8m3hLy1dNntl;-fEvcfa zduG<%-l~(uM0IPWNTO8{Y46yo_lelo{`tNIU8xVNCQw|f4{O#Mz^xsOU2XBi{6hUx 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