First draft of working init of LBM sequential

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)