diff --git a/lbm_at_diff_taxo_seq.R b/lbm_at_diff_taxo_seq.R index fdedf1f..e4b9663 100644 --- a/lbm_at_diff_taxo_seq.R +++ b/lbm_at_diff_taxo_seq.R @@ -92,11 +92,154 @@ bm_propagate_taus_max_ICL <- function(phyloseq_data) { return(result_list) } -mach_out <- bm_propagate_taus_max_ICL(phyloseq_data = the_data) +# mach_out <- bm_propagate_taus_max_ICL(phyloseq_data = the_data) -chaillou_out <- bm_propagate_taus_max_ICL(phyloseq_data = import_biom("data/chaillou/chaillou.biom")) +# chaillou_out <- bm_propagate_taus_max_ICL(phyloseq_data = import_biom("data/chaillou/chaillou.biom")) -ravel_out <- bm_propagate_taus_max_ICL(phyloseq_data = import_biom("data/ravel/ravel.biom")) +# ravel_out <- bm_propagate_taus_max_ICL(phyloseq_data = import_biom("data/ravel/ravel.biom")) # To init a BM model I need to provide memberships and ICL # And to compute with the dispatcher the previous values to init the models with ICL and memberships + + +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) { + stopifnot("Q must be provided" = !missing(Q), "pĥyloseq_data must be provided" = !missing(phyloseq_data)) + force(per_taxa_networks) + + result_list <- list() + # Init + mean_diff_vec <- c() + PL_per_nb_vec <- c() + max_rank_id <- tax_table(phyloseq_data) |> ncol() + net_id_start <- ifelse(removed_root, rank_id_start - 1, rank_id_start) + result_list <- append(result_list, first_model) + # Here we extract the memberships + current_memberships <- first_model$memberships[[Q]] + if (!is.matrix(current_memberships$Z1)) { + nrow_Z1 <- length(current_memberships$Z1) + } else { + nrow_Z1 <- nrow(current_memberships$Z1) + } + next_cc <- list(Z1 = as.matrix(current_memberships$Z1, nrow = nrow_Z1), Z2 = current_memberships$Z2) + + # Here we dispatch the memberships for the OTU + ## Propagate the rownames + rownames(next_cc$Z1) <- rownames(per_taxa_networks[[net_id_start]]) + target_net_id <- ifelse(removed_root, target_rank_id - 1, target_rank_id) + next_Z1 <- propagate_taus(tau_matrix = next_cc$Z1, physeq = phyloseq_data, taxrank = phyloseq::rank_names(phyloseq_data)[target_rank_id]) + + ## Noising of the next_Z1 + dZ1 <- dim(next_Z1) + nZ1 <- dZ1[1] * dZ1[2] + next_Z1 <- next_Z1 + matrix(rnorm(n = nZ1, mean = 0, sd = sd_noise), nrow = dZ1[1], ncol = dZ1[2]) + + ## Normalizing by rows + C_Z1 <- rowSums(next_Z1) + next_Z1 <- next_Z1 / C_Z1 + + next_cc$Z1 <- next_Z1 + + + next_res <- blockmodels:::dispatcher( + membership_name = "LBM", + membership_init = next_cc, + model_name = "poisson", + network = list(adjacency = per_taxa_networks[[target_net_id]]), real_EM = TRUE + ) # account for the removed root + result_list <- append(result_list, next_res) + mean_diff_vec[paste0(rank_id_start, "->", target_rank_id)] <- mean((next_res$membership$Z1 - next_Z1)^2) + next_cc <- next_res[["membership"]][c("Z1", "Z2")] + PL_per_nb_vec[paste0(rank_id_start, "->", target_rank_id)] <- next_res$PL / (nrow(next_res$membership$Z1) * nrow(next_res$membership$Z2)) + return(next_res) +} +#' Use a preinited model to run blockmodels exploration +#' +#' @param bm_model A pre-initialized model object +bm_estimate_with_inits <- function(bm_model, reinitialization_effort = 1) { + l <- TRUE + n <- 1 + changing_effort <- FALSE + while (l) { + cat("Pass", n, "\n") + + cat("With ascending number of groups\n") + ra <- bm_model$estim_ascend(reinitialization_effort, changing_effort) + + cat("With descending number of groups\n") + rb <- bm_model$estim_descend(reinitialization_effort) + + l <- ra || rb + n <- n + 1 + changing_effort <- FALSE + } +} + +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)) { + removed_root <- FALSE + # Detect if first rank (higher) is fully collapsed + if (nrow(per_taxa_networks[[1]]) <= 1L) { + message("The first network has only one row. Removing it.") + removed_root <- TRUE + per_taxa_networks <- per_taxa_networks[-1] + } + result_list <- list() + # Init + net_id_start <- ifelse(removed_root, rank_id_start - 1, rank_id_start) + if (missing(first_model)) { + first_model <- BM_poisson( + membership_type = "LBM", + adj = per_taxa_networks[[net_id_start]], # Account for the root + verbosity = 2, + plotting = "", + ncores = 1L + ) + first_model$estimate() + } + + propagated_models <- lapply(seq_along(first_model$ICL), function(current_q) { + if (!is.na(first_model$ICL[current_q])) { + bm_propagate_taus_Q_next_level(phyloseq_data = phyloseq_data, first_model = first_model, per_taxa_networks = per_taxa_networks, Q = current_q, removed_root = removed_root, rank_id_start = rank_id_start, target_rank_id = target_rank_id) + } else { + return(NA) + } + }) + + target_net_id <- ifelse(removed_root, target_rank_id - 1, target_rank_id) + + new_model <- BM_poisson( + membership_type = "LBM", + adj = per_taxa_networks[[target_net_id]], # Account for the root + verbosity = 6, + plotting = "", + ncores = 1L + ) + + lapply(seq_along(propagated_models), function(current_q) { + if (any(is.na(propagated_models[[current_q]]))) { + return(NA) + } + new_model$memberships[[current_q]] <<- getRefClass("LBM")(from_cc = propagated_models[[current_q]]$membership) + new_model$model_parameters[[current_q]] <- propagated_models[[current_q]]$model + new_model$PL[current_q] <- propagated_models[[current_q]]$PL + new_model$H[current_q] <- propagated_models[[current_q]]$H + new_model$ICL[current_q] <- propagated_models[[current_q]]$PL - .5 * (propagated_models[[current_q]]$model$n_parameters * + log(new_model$data_number()) + new_model$memberships[[current_q]]$ICL_penalty()) + }) + + bm_estimate_with_inits(bm_model = new_model) + + return(new_model) +} + +per_taxa_networks <- collapse_otu_at_taxo(the_data) +r2_model <- BM_poisson( + membership_type = "LBM", + adj = per_taxa_networks[[2]], # Account for the root + verbosity = 2, + plotting = "", + ncores = 1L +) +r2_model$estimate() +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) +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) +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)