Multipartite fungus tree with all dist matrix
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Louis
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65e305e297
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6fa32f2157
1 changed files with 148 additions and 22 deletions
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@ -1,6 +1,7 @@
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# library(GREMLINS) # Load the custom GREMLINS
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devtools::load_all("../GREMLINS/")
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library(sbm)
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# library(sbm)
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devtools::load_all("../sbm/")
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data(fungusTreeNetwork)
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tree_genetic_dist_matrix <- fungusTreeNetwork$covar_tree$genetic_dist
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@ -26,34 +27,159 @@ v_distrib <- c("gaussian", "bernoulli")
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namesFG <- c("Tree", "Fungus")
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givenclassif <- list(tree_geo_sbm$memberships, fungus_tree_sbm$memberships[["row"]])
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geo_multi_noinit <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = TRUE, verbose = TRUE)
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# geo_multi_noinit <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = TRUE, verbose = TRUE)
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Tree_Geo_dist_sbm <- defineSBM(-fungusTreeNetwork$covar_tree$geographic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
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Tree_Taxo_dist_sbm <- defineSBM(-fungusTreeNetwork$covar_tree$taxonomic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
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Tree_Genetic_dist_sbm <- defineSBM(fungusTreeNetwork$covar_tree$genetic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
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Tree_Genetic_dist_sbm <- defineSBM(-fungusTreeNetwork$covar_tree$genetic_dist, model = "gaussian", type = "simple", dimLabels = c("Tree"))
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FungusTree_sbm <- defineSBM(fungusTreeNetwork$fungus_tree, dimLabels = c("Fungus", "Tree"), model = "bernoulli", type = "bipartite")
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geo_multi_noinit_sbm <- estimateMultipartiteSBM(list(Tree_Geo_dist_sbm, FungusTree_sbm), estimOptions = list(initBM = TRUE, ))
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# geo_multi_noinit_sbm <- estimateMultipartiteSBM(list(Tree_Geo_dist_sbm, FungusTree_sbm), estimOptions = list(initBM = TRUE))
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geo_multi_noinit_nobm <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE)
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# Les kmins donnent le même résultat ICL -449.21
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geo_multi_noinit_nobm_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE, v_Kmin = c(7, 2))
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geo_multi_noinit_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = TRUE, verbose = TRUE, v_Kmin = c(7, 2))
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geo_multi_init_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = givenclassif, v_Kmin = c(7, 2)) # Ne s'autorise pas à descendre
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##
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# geo_multi_noinit_nobm <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE)
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# # Les kmins donnent le même résultat ICL -449.21
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# geo_multi_noinit_nobm_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = FALSE, verbose = TRUE, v_Kmin = c(7, 2))
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# geo_multi_noinit_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = NULL, initBM = TRUE, verbose = TRUE, v_Kmin = c(7, 2))
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# geo_multi_init_kmin <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = givenclassif, v_Kmin = c(7, 2)) # Ne s'autorise pas à descendre
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# ##
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# Le meilleur ICL est ici : -408.05 avec une classif de base issue de blockmodels indépendants
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geo_multi_init <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = givenclassif)
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# # Le meilleur ICL est ici : -408.05 avec une classif de base issue de blockmodels indépendants
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# geo_multi_init <- multipartiteBM(list_Net = list_Net, v_distrib = v_distrib, namesFG = namesFG, givenclassif = givenclassif)
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library(knitr)
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# library(knitr)
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kable(data.frame("Given classif" = c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE), "InitBM" = c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE), "Kmin given" = c(FALSE, FALSE, TRUE, TRUE, TRUE, FALSE), ICL = c(
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geo_multi_noinit$fittedModel[[1]]$ICL,
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geo_multi_noinit_nobm$fittedModel[[1]]$ICL,
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geo_multi_noinit_nobm_kmin$fittedModel[[1]]$ICL,
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geo_multi_noinit_kmin$fittedModel[[1]]$ICL,
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geo_multi_init_kmin$fittedModel[[1]]$ICL,
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geo_multi_init$fittedModel[[1]]$ICL
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)), format = "markdown")
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# kable(data.frame("Given classif" = c(FALSE, FALSE, FALSE, FALSE, TRUE, TRUE), "InitBM" = c(TRUE, FALSE, FALSE, TRUE, TRUE, TRUE), "Kmin given" = c(FALSE, FALSE, TRUE, TRUE, TRUE, FALSE), ICL = c(
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# geo_multi_noinit$fittedModel[[1]]$ICL,
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# geo_multi_noinit_nobm$fittedModel[[1]]$ICL,
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# geo_multi_noinit_nobm_kmin$fittedModel[[1]]$ICL,
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# geo_multi_noinit_kmin$fittedModel[[1]]$ICL,
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# geo_multi_init_kmin$fittedModel[[1]]$ICL,
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# geo_multi_init$fittedModel[[1]]$ICL
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# )), format = "markdown")
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dataR6 <- formattingData(list_Net, v_distrib)
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conditions <- expand.grid(given = c(TRUE, FALSE), initBM = c(TRUE, FALSE), Kmin = c(TRUE, FALSE), rep = seq(3))
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library(future.apply)
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library(future.callr)
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plan("multisession")
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results_geo_df <- do.call("rbind", future_lapply(seq_len(nrow(conditions)), function(row_idx) {
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initBM <- conditions[row_idx, "initBM"]
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is_given <- conditions[row_idx, "given"]
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is_Kmin <- conditions[row_idx, "Kmin"]
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rep <- conditions[row_idx, "rep"]
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if (is_given) {
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the_givenclassif <- givenclassif
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} else {
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the_givenclassif <- NULL
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}
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if (is_Kmin) {
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the_Kmin <- list(seq(7, 10), seq(2, 10))
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} else {
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the_Kmin <- list(seq(10), seq(10))
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}
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names(the_Kmin) <- c("Tree", "Fungus")
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fit <- estimateMultipartiteSBM(
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listSBM = list(Tree_Geo_dist_sbm, FungusTree_sbm),
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estimOptions = list(
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"initBM" = initBM,
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"givenclassif" = the_givenclassif,
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"nbBlocksRange" = the_Kmin,
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maxVEM = 1000,
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maxVE = 1000
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)
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)
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out <- data.frame(
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InitBM = initBM,
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Kmin = is_Kmin,
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GivenClassif = is_given,
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rep = rep,
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ICL = fit$ICL,
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nbBlocksTree = fit$nbBlocks["Tree"], nbBlocksFungus = fit$nbBlocks["Fungus"]
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)
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}, future.seed = TRUE))
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# future_
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results_taxo_df <- do.call("rbind", lapply(seq_len(nrow(conditions)), function(row_idx) {
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initBM <- conditions[row_idx, "initBM"]
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is_given <- conditions[row_idx, "given"]
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is_Kmin <- conditions[row_idx, "Kmin"]
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rep <- conditions[row_idx, "rep"]
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if (is_given) {
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the_givenclassif <- givenclassif
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} else {
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the_givenclassif <- NULL
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}
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if (is_Kmin) {
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the_Kmin <- list(seq(1, 10), seq(1, 10))
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} else {
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the_Kmin <- list(seq(10), seq(10))
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}
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names(the_Kmin) <- c("Tree", "Fungus")
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fit <- estimateMultipartiteSBM(
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listSBM = list(Tree_Taxo_dist_sbm, FungusTree_sbm),
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estimOptions = list(
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"initBM" = initBM,
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"givenclassif" = the_givenclassif,
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"nbBlocksRange" = the_Kmin,
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maxVEM = 100000,
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maxVE = 100000
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)
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)
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out <- data.frame(
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InitBM = initBM,
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Kmin = is_Kmin,
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GivenClassif = is_given,
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rep = rep,
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ICL = fit$ICL,
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nbBlocksTree = fit$nbBlocks["Tree"], nbBlocksFungus = fit$nbBlocks["Fungus"]
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)
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})) # , future.seed = TRUE))
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results_genetic_df <- do.call("rbind", lapply(seq_len(nrow(conditions)), function(row_idx) {
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initBM <- conditions[row_idx, "initBM"]
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is_given <- conditions[row_idx, "given"]
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is_Kmin <- conditions[row_idx, "Kmin"]
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rep <- conditions[row_idx, "rep"]
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if (is_given) {
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the_givenclassif <- givenclassif
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} else {
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the_givenclassif <- NULL
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}
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if (is_Kmin) {
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the_Kmin <- list(seq(4, 10), seq(2, 10))
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} else {
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the_Kmin <- list(seq(10), seq(10))
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}
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names(the_Kmin) <- c("Tree", "Fungus")
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fit <- estimateMultipartiteSBM(
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listSBM = list(Tree_Genetic_dist_sbm, FungusTree_sbm),
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estimOptions = list(
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"initBM" = initBM,
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"givenclassif" = the_givenclassif,
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"nbBlocksRange" = the_Kmin,
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maxVEM = 100000,
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maxVE = 100000
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)
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)
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out <- data.frame(
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InitBM = initBM,
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Kmin = is_Kmin,
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GivenClassif = is_given,
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rep = rep,
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ICL = fit$ICL,
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nbBlocksTree = fit$nbBlocks["Tree"], nbBlocksFungus = fit$nbBlocks["Fungus"]
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)
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})) # , future.seed = TRUE))
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