From 993f4ef7e49b5b7c148d34d49df2933a5a78b23a Mon Sep 17 00:00:00 2001 From: Louis Lacoste Date: Tue, 7 May 2024 16:40:47 +0200 Subject: [PATCH] Divers tests --- .../investigating/invest_clust_perf.R | 47 + .../invest_inference_bernoulli_double_ARI.Rmd | 182 +- ...invest_inference_bernoulli_double_ARI.html | 5115 +++++++++++++++++ .../profiling_parallelization_speed.R | 42 + ...ulli_inference_24-04-2024_17-14-46_1-1.Rds | Bin 0 -> 460 bytes 5 files changed, 5380 insertions(+), 6 deletions(-) create mode 100644 code/analysis/investigating/invest_clust_perf.R create mode 100644 code/analysis/investigating/invest_inference_bernoulli_double_ARI.html create mode 100644 code/analysis/investigating/profiling_parallelization_speed.R create mode 100644 code/results/simulations/inference/bernoulli/bernoulli_inference_24-04-2024_17-14-46_1-1.Rds diff --git a/code/analysis/investigating/invest_clust_perf.R b/code/analysis/investigating/invest_clust_perf.R new file mode 100644 index 0000000..649291c --- /dev/null +++ b/code/analysis/investigating/invest_clust_perf.R @@ -0,0 +1,47 @@ +library(colSBM) +library(microbenchmark) +library(here) + +set.seed(1234, "L'Ecuyer-CMRG") +data(dorebipartite) +str(dorebipartite) + +nb_cores = parallelly::availableCores() +concurrent_models <- min(length(seq(1,4)), nb_cores%/%2) +per_model <- (nb_cores - concurrent_models) %/% length(seq(1,4)) + +mb1 <- microbenchmark( + "fancy computation" = { + parallel::mclapply(seq(1, 4), function(idx) { + message("Start ", idx) + estimate_colBiSBM( + netlist = dorebipartite[1:4], + colsbm_model = "iid", + global_opts = list(nb_cores = per_model, verbosity = 0L) + ) + message("End ", idx) + }, + mc.cores = concurrent_models + ) + }, + times = 5L +) + +mb2 <- microbenchmark( + "Attributing all" = { + parallel::mclapply(seq(1, 4), function(idx) { + message("Start ", idx) + estimate_colBiSBM( + netlist = dorebipartite[1:4], + colsbm_model = "iid", + global_opts = list(nb_cores = parallelly::availableCores(omit = 1), verbosity = 1L) + ) + message("End ", idx) + }, + mc.cores = parallelly::availableCores(omit = 1) + ) + }, + times = 5L +) + +# Bilan : plus intéressant de bourriner \ No newline at end of file diff --git a/code/analysis/investigating/invest_inference_bernoulli_double_ARI.Rmd b/code/analysis/investigating/invest_inference_bernoulli_double_ARI.Rmd index 0a7e782..f8ed5a1 100644 --- a/code/analysis/investigating/invest_inference_bernoulli_double_ARI.Rmd +++ b/code/analysis/investigating/invest_inference_bernoulli_double_ARI.Rmd @@ -1,9 +1,179 @@ -```{r, echo = FALSE} -data <- readRDS("code/results//simulations/inference//bernoulli/bernoulli_inference_18-04-2024_09-41-45_1-972.Rds") - -prob_data <- data[which((data$pirho_double_row_ARI < 1 | data$pirho_double_col_ARI < 1)&(data$pirho_mean_row_ARI == 1 & data$pirho_mean_col_ARI == 1)),] +```{r libraries, echo = FALSE} +library(here) +library(dplyr) +library(colSBM) +library(aricode) +library(patchwork) +library(ggplot2) ``` -```{r , echo = FALSE} -knitr::kable(prob_data) +```{r plot-alpha, echo = FALSE} +plot_alpha <- function(alpha) { + p_alpha <- alpha[drop = FALSE] %>% + t() %>% + reshape2::melt() %>% + ggplot2::ggplot(ggplot2::aes(x = Var1, y = Var2, fill = value)) + + ggplot2::geom_tile() + + ggplot2::geom_text(ggplot2::aes(label = round(value, 2)), color = "black") + + ggplot2::scale_fill_gradient2("alpha", + low = "white", + high = "red", + limits = c( + 0, + 1 + ) + ) + + ggplot2::geom_hline(yintercept = seq(nrow(alpha)) + .5) + + ggplot2::geom_vline(xintercept = seq(ncol(alpha)) + .5) + + ggplot2::scale_y_reverse() + + ggplot2::theme_bw(base_size = 15, base_rect_size = 1, base_line_size = 1) + + ggplot2::xlab("") + + ggplot2::ylab("") + + ggplot2::coord_fixed(expand = FALSE) + return(p_alpha) +} +``` + +```{r, echo = FALSE} +data_folder <- file.path( + here(), "code", "results", + "investigating", "selection_error", "incorrect24-04-2024_17-18-42" +) + +file_list <- list.files(data_folder) +file <- file_list[[1]] +data <- readRDS(file.path(data_folder, file)) + +#  Extracting data to netlist and Z +netlist_memb <- data[["netlist"]] +netlist <- lapply(netlist_memb, function(full) full[["incidence_matrix"]]) +nr <- nrow(netlist[[1]]) +nc <- ncol(netlist[[1]]) + +row_blockmemberships <- lapply(netlist_memb, function(full) full[["row_blockmemberships"]]) +col_blockmemberships <- lapply(netlist_memb, function(full) full[["col_blockmemberships"]]) + +joined_row_memberships <- unlist(row_blockmemberships) +joined_col_memberships <- unlist(col_blockmemberships) + +row_taus <- lapply(row_blockmemberships, function(Z1_m) colSBM:::.one_hot(Z1_m, 4)) +col_taus <- lapply(col_blockmemberships, function(Z2_m) colSBM:::.one_hot(Z2_m, 4)) + + +``` + +```{r parameters, echo = FALSE} +set.seed(1234, "L'Ecuyer-CMRG") +fitted_bisbmpop_pirho <- estimate_colBiSBM( + netlist = netlist, + colsbm_model = "pirho", + nb_run = 3L, + distribution = "bernoulli", + global_opts = list( + verbosity = 3L, + plot_details = 0, + nb_cores = parallelly::availableCores(omit = 1) + ) +) +``` + +```{r, echo = FALSE} +bad_model <- fitted_bisbmpop_pirho[["best_fit"]]$clone() +good_model <- fitted_bisbmpop_pirho[["model_list"]][[4, 4]]$clone() +``` + +```{r ARI, echo = FALSE} +good_Z <- good_model[["Z"]] +good_row <- lapply(good_Z, function(model) model[["row"]]) +good_col <- lapply(good_Z, function(model) model[["col"]]) + +good_joined_row <- unlist(good_row) +good_joined_col <- unlist(good_col) + +sapply(seq_len(length(good_row)), function(m) { + ARI( + row_blockmemberships[[m]], + good_row[[m]] + ) +}) + +sapply(seq_len(length(good_row)), function(m) { + ARI( + col_blockmemberships[[m]], + good_col[[m]] + ) +}) + +ARI(joined_row_memberships, good_joined_row) +ARI(joined_col_memberships, good_joined_col) +``` + +```{r, echo = FALSE} +wrap_plots( + plot_alpha(data[["alpha"]])+ labs(caption = "Vrai alpha"), + plot(good_model, type = "meso", mixture = TRUE, values = TRUE) + labs(caption = "Q = (4,4)"), + plot(bad_model, type = "meso", mixture = TRUE, values = TRUE) + labs(caption = "Q = (4,5)") +) +``` + +```{r poisson?, echo = FALSE} +# set.seed(1234, "L'Ecuyer-CMRG") +# alpha_poisson <- alpha * 10 + +# netlist_poisson <- c(generate_bipartite_collection(nr = nr, nc = nc, pi = pi1, rho = rho1, alpha = alpha_poisson, M = 1, distribution = "poisson"), +# generate_bipartite_collection(nr = nr, nc = nc, pi = pi2, rho = rho2, alpha = alpha_poisson, M = 1, distribution = "poisson")) + +# fitted_bisbmpop_pirho_poisson <- estimate_colBiSBM( +# netlist = netlist_poisson, +# colsbm_model = "pirho", +# nb_run = 1, +# distribution = "poisson", +# global_opts = list( +# verbosity = 3L, +# plot_details = 0, +# nb_cores = parallelly::availableCores(omit = 1) +# ) +# ) +# wrap_plots( +# plot_alpha(alpha_poisson) + labs(caption = "Vrai alpha"), +# plot(fitted_bisbmpop_pirho_poisson$best_fit, type = "meso", values = TRUE, mixture = TRUE)) +``` + +Ici on clone le modèle et on lui donne les bons paramètres pour voir s'il fait +mieux que celui trouvé avant. + +```{r testing_true_params, echo = FALSE} +# Un clone +good_clone <- good_model$clone() + +# Les vrais paramètres +alpha <- data[["alpha"]] +taus <- lapply(seq_along(row_taus), function(m) { + list(row = row_taus[[m]], + col = col_taus[[m]]) +}) +# Pi params +pi1 <- as.vector(data[["pi1"]]) +pi1[1] <- pi1[1] + 1e-9 +rho1 <- rep(0.25, 4) +pi2 <- rep(0.25, 4) +rho2 <- as.vector(data[["rho2"]]) +rho2[3] <- rho2[3] + 1e-9 +pi <- list(list(pi1, rho1), list(pi2, rho2)) + +good_clone[["tau"]] <- taus +good_clone[["pi"]] <- pi +good_clone[["alpha"]] <- alpha + +result_BICL <- c(bad_BICL = bad_model$compute_BICL(), +good_model_BICL = good_model$compute_BICL(), +good_clone_BICL = good_clone$compute_BICL()) +print(result_BICL) +``` + +On vient mettre à la place du 4,4 le modèle avec les vrais paramètres. +Et on espère voir la bonne information se diffuser. +```{r adjusting, echo = FALSE} +fitted_bisbmpop_pirho$model_list[[4,4]] <- good_clone +fit_1_pass <- adjust_colBiSBM(fitted_bisbmpop_pirho, Q = c(4,4), depth = 1L, nb_pass = 1L) ``` \ No newline at end of file diff --git a/code/analysis/investigating/invest_inference_bernoulli_double_ARI.html b/code/analysis/investigating/invest_inference_bernoulli_double_ARI.html new file mode 100644 index 0000000..34b79f4 --- /dev/null +++ b/code/analysis/investigating/invest_inference_bernoulli_double_ARI.html @@ -0,0 +1,5115 @@ + + + + + + + + + + + + + +invest_inference_bernoulli_double_ARI.knit + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
## here() starts at /home/polarolouis/Documents/Temporary Projects/mia-stage-2024
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epsilon_alphapi1.1pi1.2pi1.3pi1.4rho2.1rho2.2rho2.3rho2.4repetitionsep_BICLsep_mean_row_ARIsep_mean_col_ARIsep_double_row_ARIsep_double_col_ARIiid_BICLiid_mean_row_ARIiid_mean_col_ARIiid_double_row_ARIiid_double_col_ARIiid_Q1iid_Q2pi_BICLpi_mean_row_ARIpi_mean_col_ARIpi_double_row_ARIpi_double_col_ARIpi_Q1pi_Q2rho_BICLrho_mean_row_ARIrho_mean_col_ARIrho_double_row_ARIrho_double_col_ARIrho_Q1rho_Q2pirho_BICLpirho_mean_row_ARIpirho_mean_col_ARIpirho_double_row_ARIpirho_double_col_ARIpirho_Q1pirho_Q2elapsed_secs
5550.150.00.40.20.40.33333330.33333330.00000000.33333333-17326.431.00000010.63540380.3475734-17372.740.89939830.95772380.46750790.408208445-17347.481.00000000.93345670.53107440.314500764-17326.570.91178110.98829420.51034700.437615345-17328.68110.41189670.4308905551027.1878 secs
5570.150.00.40.20.40.33333330.33333330.33333330.00000002-17661.351.00000010.56110580.3683275-17676.781.00000000.98888161.00000000.982912644-17655.461.00000000.98888161.00000000.982912644-17685.010.99685131.00000000.52023820.483686645-17651.53110.68896360.4588394451677.9255 secs
5680.150.20.00.40.40.00000000.33333330.33333330.33333331-17351.631.00000010.36142460.4932339-17421.390.98745340.95848190.38740320.319087154-17371.861.00000000.96473750.39951570.322912854-17366.990.90158781.00000000.29956100.525362246-17341.95110.45948110.6009543541095.9794 secs
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6530.180.00.20.40.40.33333330.33333330.00000000.33333332-16525.571.00000010.75609130.3756357-16631.780.97240330.99559230.38516510.440941045-16542.311.00000000.88021410.55318960.297570764-16530.550.99376261.00000000.36564310.488640745-16530.44110.46153420.445801655753.9150 secs
6560.180.00.20.40.40.33333330.33333330.33333330.00000002-17111.041.00000010.39415030.3792336-17138.161.00000000.98758401.00000000.980982644-17111.601.00000000.98758401.00000000.980982644-17117.901.00000001.00000001.00000001.000000044-17101.76110.51916680.509608945990.8326 secs
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6610.180.00.40.20.40.33333330.33333330.00000000.33333331-16750.091.00000010.69824250.6363895-16817.570.96746920.99351060.65307040.428250645-16781.250.99494490.98728540.51855190.340262264-16744.850.99151151.00000000.98989781.000000044-16741.69110.69543220.432268745857.1393 secs
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6760.180.20.00.40.40.00000000.33333330.33333330.33333331-16703.851.00000010.59263630.6435837-16774.470.93163280.99231110.41638250.511886554-16713.830.98738130.99231110.42950210.511886554-16691.691.00000001.00000001.00000001.000000044-16708.29110.42116880.415525555954.6656 secs
6770.180.20.00.40.40.00000000.33333330.33333330.33333332-16683.471.00000010.41075040.5398208-16773.161.00000000.99493990.41758130.344598054-16708.731.00000000.96780370.53148580.325523064-16707.290.99307341.00000000.36710740.477533846-16688.20110.41758130.405047655863.7348 secs
6780.180.20.00.40.40.00000000.33333330.33333330.33333333-16750.451.00000010.63803910.7694721-16825.110.94730761.00000000.47035310.562238354-16765.221.00000000.99377690.48265740.552841554-16770.780.95150871.00000000.34460630.537737946-16740.48110.48265740.562238354787.8910 secs
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6840.180.20.00.40.40.33333330.33333330.33333330.00000003-16359.261.00000010.37689920.6320516-16442.710.97910170.97170610.39020560.337594854-16375.461.00000000.93540930.50492170.303822764-16390.250.98768221.00000000.37210380.518180946-16361.35110.45352900.438474155953.8394 secs
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7190.180.40.00.40.20.33333330.33333330.33333330.00000002-16746.681.00000010.67557801.0000000-16760.251.00000001.00000001.00000001.000000044-16767.011.00000000.94372300.48150410.320858764-16779.300.96370971.00000000.39713960.528420446-16750.09110.42226290.4326288551067.0659 secs
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7490.180.40.40.20.00.00000000.33333330.33333330.33333332-16717.471.00000010.60264020.5794619-16778.110.98062461.00000000.41298230.564861454-16731.011.00000000.98313090.41264210.537301354-16747.020.98796321.00000000.47323580.757413946-16709.14110.41264210.5648614541029.9068 secs
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7580.210.00.20.40.40.33333330.00000000.33333330.33333332-15463.681.00000010.37864360.3829776-15603.100.95694770.97317630.66351310.706612546-15464.321.00000001.00000001.00000001.000000044-15482.450.92921451.00000000.33087580.543339646-15466.96110.42402390.4368330551323.6269 secs
7610.210.00.20.40.40.33333330.33333330.00000000.33333332-15747.531.00000010.48626120.6254415-15876.850.92086090.99318970.37203700.440315655-15778.521.00000000.97434140.49310170.359189564-15766.560.91464821.00000000.30722350.551753246-15751.86110.43910250.463566455875.4299 secs
7620.210.00.20.40.40.33333330.33333330.00000000.33333333-15593.911.00000010.47102420.3537180-15665.150.99190351.00000000.44663810.449063045-15622.111.00000000.97096410.48783750.349545864-15601.950.99190351.00000000.44663810.449063045-15584.32110.46107050.449063045836.4729 secs
7650.210.00.20.40.40.33333330.33333330.33333330.00000003-15862.041.00000010.70492920.3695618-15930.651.00000000.97510630.62402370.400163345-15856.631.00000001.00000001.00000001.000000044-15860.261.00000001.00000001.00000001.000000044-15854.26110.62402370.400163345983.0682 secs
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7870.210.20.00.40.40.33333330.33333330.00000000.33333331-15212.441.00000010.38838650.3716715-15402.150.93577860.99434470.37348010.426077855-15210.071.00000000.99511211.00000000.992510744-15214.261.00000001.00000001.00000001.000000044-15215.99110.45318930.4651698551296.5766 secs
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+ + + + +
+ + + + + + + + + + + + + + + diff --git a/code/analysis/investigating/profiling_parallelization_speed.R b/code/analysis/investigating/profiling_parallelization_speed.R new file mode 100644 index 0000000..cee0216 --- /dev/null +++ b/code/analysis/investigating/profiling_parallelization_speed.R @@ -0,0 +1,42 @@ +library(colSBM) +library(here) + +set.seed(1234, "L'Ecuyer-CMRG") +prof_folder <- file.path(here(), "code", "results", "investigating", "profiling") +if (!dir.exists(prof_folder)) { + dir.create(prof_folder) +} +prof_file <- file.path(prof_folder, "no-parallel-asis.out") + +nr <- 70 +nc <- 50 + +pi <- c(0.3, 0.2, 0.5) +rho <- c(0.25, 0.15, 0.6) + +alpha <- matrix(c( + 0.75, 0.4, 0.1, + 0.2, 0.05, 0.05, + 0.1, 0.05, 0.3 +), nrow = 3) + +netlist <- generate_bipartite_collection( + nr = nr, nc = nc, + pi = pi, rho = rho, + alpha = alpha, + M = 4 +) + +Rprof(filename = prof_file, memory.profiling = TRUE) +fit <- estimate_colBiSBM( + netlist = netlist, + colsbm_model = "pirho", + global_opts = list(verbosity = 1L, backend = "no_mc") +) +Rprof(NULL) + +for (filename in list.files(prof_folder)) { + print(filename) + prof_sum <- summaryRprof(file.path(prof_folder, filename), memory = "both") + print(prof_sum[["by.total"]][1, ]) +} diff --git a/code/results/simulations/inference/bernoulli/bernoulli_inference_24-04-2024_17-14-46_1-1.Rds b/code/results/simulations/inference/bernoulli/bernoulli_inference_24-04-2024_17-14-46_1-1.Rds new file mode 100644 index 0000000000000000000000000000000000000000..a76d99a2c36f2ed4c7745d05de0533267d152df2 GIT binary patch literal 460 zcmV;-0W7pFUQqHPi=lf6cE0?d|@4_*g!-N*dCfZtl-`t>|1KF42T{&4i@e?ARf{_1xT z4@VFCANAYsWt}Jco%jFWZ#n(6kPmmapRYnrCBym@$dT`K+k(DN9*UfpHF2jC;2x5b z`!){blK}cDi1Xt_NI{Wx=uANil{13 zO(0g6_mGPM9AL+0Kh{UCaojjLn{ttI4?)}ToEzilyfLS@9gEXTR5zXewS}Y#J|fm+ zF*b#slvO=rUFu17bx$rL)?{d;sSCs=w&G&m&N@Mbn^+bP-XOBl3l|V8$eWxw34s~O zl9we6hO%lfu`UgU>gvI85os_q($ocFe)2As9F-zP;fark9k!x~(w7*M{w7f@_QsgG zTSXRhePkNZ6%?*tj