First draft of working init of LBM sequential
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Louis
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1 changed files with 146 additions and 3 deletions
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@ -92,11 +92,154 @@ bm_propagate_taus_max_ICL <- function(phyloseq_data) {
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return(result_list)
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}
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mach_out <- bm_propagate_taus_max_ICL(phyloseq_data = the_data)
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# mach_out <- bm_propagate_taus_max_ICL(phyloseq_data = the_data)
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chaillou_out <- bm_propagate_taus_max_ICL(phyloseq_data = import_biom("data/chaillou/chaillou.biom"))
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# chaillou_out <- bm_propagate_taus_max_ICL(phyloseq_data = import_biom("data/chaillou/chaillou.biom"))
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ravel_out <- bm_propagate_taus_max_ICL(phyloseq_data = import_biom("data/ravel/ravel.biom"))
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# ravel_out <- bm_propagate_taus_max_ICL(phyloseq_data = import_biom("data/ravel/ravel.biom"))
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# To init a BM model I need to provide memberships and ICL
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# And to compute with the dispatcher the previous values to init the models with ICL and memberships
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bm_propagate_taus_Q_next_level <- function(phyloseq_data, first_model, Q, per_taxa_networks = collapse_otu_at_taxo(phyloseq_data), rank_id_start = 2L, target_rank_id = rank_id_start + 1, removed_root = TRUE, sd_noise = 0.001) {
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stopifnot("Q must be provided" = !missing(Q), "pĥyloseq_data must be provided" = !missing(phyloseq_data))
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force(per_taxa_networks)
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result_list <- list()
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# Init
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mean_diff_vec <- c()
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PL_per_nb_vec <- c()
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max_rank_id <- tax_table(phyloseq_data) |> ncol()
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net_id_start <- ifelse(removed_root, rank_id_start - 1, rank_id_start)
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result_list <- append(result_list, first_model)
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# Here we extract the memberships
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current_memberships <- first_model$memberships[[Q]]
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if (!is.matrix(current_memberships$Z1)) {
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nrow_Z1 <- length(current_memberships$Z1)
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} else {
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nrow_Z1 <- nrow(current_memberships$Z1)
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}
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next_cc <- list(Z1 = as.matrix(current_memberships$Z1, nrow = nrow_Z1), Z2 = current_memberships$Z2)
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# Here we dispatch the memberships for the OTU
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## Propagate the rownames
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rownames(next_cc$Z1) <- rownames(per_taxa_networks[[net_id_start]])
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target_net_id <- ifelse(removed_root, target_rank_id - 1, target_rank_id)
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next_Z1 <- propagate_taus(tau_matrix = next_cc$Z1, physeq = phyloseq_data, taxrank = phyloseq::rank_names(phyloseq_data)[target_rank_id])
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## Noising of the next_Z1
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dZ1 <- dim(next_Z1)
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nZ1 <- dZ1[1] * dZ1[2]
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next_Z1 <- next_Z1 + matrix(rnorm(n = nZ1, mean = 0, sd = sd_noise), nrow = dZ1[1], ncol = dZ1[2])
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## Normalizing by rows
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C_Z1 <- rowSums(next_Z1)
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next_Z1 <- next_Z1 / C_Z1
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next_cc$Z1 <- next_Z1
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next_res <- blockmodels:::dispatcher(
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membership_name = "LBM",
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membership_init = next_cc,
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model_name = "poisson",
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network = list(adjacency = per_taxa_networks[[target_net_id]]), real_EM = TRUE
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) # account for the removed root
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result_list <- append(result_list, next_res)
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mean_diff_vec[paste0(rank_id_start, "->", target_rank_id)] <- mean((next_res$membership$Z1 - next_Z1)^2)
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next_cc <- next_res[["membership"]][c("Z1", "Z2")]
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PL_per_nb_vec[paste0(rank_id_start, "->", target_rank_id)] <- next_res$PL / (nrow(next_res$membership$Z1) * nrow(next_res$membership$Z2))
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return(next_res)
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}
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#' Use a preinited model to run blockmodels exploration
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#'
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#' @param bm_model A pre-initialized model object
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bm_estimate_with_inits <- function(bm_model, reinitialization_effort = 1) {
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l <- TRUE
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n <- 1
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changing_effort <- FALSE
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while (l) {
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cat("Pass", n, "\n")
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cat("With ascending number of groups\n")
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ra <- bm_model$estim_ascend(reinitialization_effort, changing_effort)
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cat("With descending number of groups\n")
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rb <- bm_model$estim_descend(reinitialization_effort)
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l <- ra || rb
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n <- n + 1
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changing_effort <- FALSE
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}
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}
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bm_propagate_taus_all_models <- function(phyloseq_data, rank_id_start = 2, target_rank_id = rank_id_start + 1, first_model = NULL, per_taxa_networks = collapse_otu_at_taxo(phyloseq_data)) {
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removed_root <- FALSE
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# Detect if first rank (higher) is fully collapsed
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if (nrow(per_taxa_networks[[1]]) <= 1L) {
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message("The first network has only one row. Removing it.")
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removed_root <- TRUE
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per_taxa_networks <- per_taxa_networks[-1]
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}
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result_list <- list()
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# Init
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net_id_start <- ifelse(removed_root, rank_id_start - 1, rank_id_start)
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if (missing(first_model)) {
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first_model <- BM_poisson(
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membership_type = "LBM",
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adj = per_taxa_networks[[net_id_start]], # Account for the root
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verbosity = 2,
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plotting = "",
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ncores = 1L
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)
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first_model$estimate()
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}
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propagated_models <- lapply(seq_along(first_model$ICL), function(current_q) {
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if (!is.na(first_model$ICL[current_q])) {
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bm_propagate_taus_Q_next_level(phyloseq_data = phyloseq_data, first_model = first_model, per_taxa_networks = per_taxa_networks, Q = current_q, removed_root = removed_root, rank_id_start = rank_id_start, target_rank_id = target_rank_id)
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} else {
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return(NA)
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}
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})
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target_net_id <- ifelse(removed_root, target_rank_id - 1, target_rank_id)
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new_model <- BM_poisson(
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membership_type = "LBM",
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adj = per_taxa_networks[[target_net_id]], # Account for the root
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verbosity = 6,
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plotting = "",
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ncores = 1L
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)
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lapply(seq_along(propagated_models), function(current_q) {
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if (any(is.na(propagated_models[[current_q]]))) {
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return(NA)
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}
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new_model$memberships[[current_q]] <<- getRefClass("LBM")(from_cc = propagated_models[[current_q]]$membership)
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new_model$model_parameters[[current_q]] <- propagated_models[[current_q]]$model
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new_model$PL[current_q] <- propagated_models[[current_q]]$PL
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new_model$H[current_q] <- propagated_models[[current_q]]$H
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new_model$ICL[current_q] <- propagated_models[[current_q]]$PL - .5 * (propagated_models[[current_q]]$model$n_parameters *
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log(new_model$data_number()) + new_model$memberships[[current_q]]$ICL_penalty())
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})
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bm_estimate_with_inits(bm_model = new_model)
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return(new_model)
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}
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per_taxa_networks <- collapse_otu_at_taxo(the_data)
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r2_model <- BM_poisson(
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membership_type = "LBM",
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adj = per_taxa_networks[[2]], # Account for the root
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verbosity = 2,
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plotting = "",
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ncores = 1L
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)
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r2_model$estimate()
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r3_model <- bm_propagate_taus_all_models(phyloseq_data = the_data, rank_id_start = 2, target_rank_id = 3, per_taxa_networks = per_taxa_networks)
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r4_model <- bm_propagate_taus_all_models(phyloseq_data = the_data, rank_id_start = 3, per_taxa_networks = per_taxa_networks, first_model = r3_model)
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r5_model <- bm_propagate_taus_all_models(phyloseq_data = the_data, rank_id_start = 4, per_taxa_networks = per_taxa_networks, first_model = r4_model)
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