From 0da3dfe1a45d927f097a697f4d2481dd6e3343fd Mon Sep 17 00:00:00 2001 From: hyunjimoon Date: Sun, 8 Aug 2021 08:43:05 +0900 Subject: [PATCH 1/5] true paramete-rwise SBC --- R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R | 173 +++++++++++++++--- R/2_Theta_bar_Y/DtrMngSep/DMsep_5param_test.R | 69 ++++--- .../DtrMngSep/models/DMSep/DMSep.stan | 16 +- 3 files changed, 192 insertions(+), 66 deletions(-) diff --git a/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R b/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R index d5694d7..4601924 100644 --- a/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R +++ b/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R @@ -1,26 +1,56 @@ +devtools::install_github("hyunjimoon/SBC",ref="api-variant") library(SBC) library(mice) library(cmdstanr) library(dplyr) +library("ConnMatTools") source(file.path(getwd(), "R/1_Y_bar_y/impute/engine_fail_imputation.R")) source("R/utils/functions.r") +source("R/2_Theta_bar_Y/DtrMngSep/DMsep_5param_test.R") +modelName = "DMSep" +Dir <- set_get_Dir(modelName, "~/Dropbox/21S_paper/defense-reliability/R/2_Theta_bar_Y/DtrMngSep") +DMSep = cmdstanr::cmdstan_model(Dir$file) + +mice_imp <- generateMice() +imputed_data<- complete(mice_imp, 1) +imputed_data_gp <- read.csv("data/y_pred_5var.csv")[,-1] +imputed_data$y_data <- unlist(c(imputed_data_gp)) +n_state = 3 +initial_state = 1 +generate_state_matrix <- function(data, n){ + #state <- cut(data, breaks=c(0, 80, 160, max(data)), labels=1:n, include.lowest = TRUE) + state <- cut(data, breaks=quantile(data,c(0,1/3,2/3,1)), labels=1:n, include.lowest = TRUE) + state<-as.numeric(state) + matrix(state,nrow=31) +} +state_matrix <- generate_state_matrix(imputed_data$y_data, n_state) +states <- as.vector(t(state_matrix)) + +iter=2000 +MSE_df<-data.frame(index=rep(0,iter),p=rep(0,iter),q=rep(0,iter),train_MSE=rep(0,iter),test_MSE=rep(0,iter)) +rate_array<-data.frame(period=c(),index=c(),rate=c()) +D_array <- array(0, dim=c(iter,4,3,3)) +ship_ind_df<-matrix(0,nrow=iter,ncol=5) # Three simulators for prior, data, posterior # 1. prior simulator -true <- as_tibble(read.csv("./data/DMSep_validation_trueparam.csv")) -# 2. data simulator -# DGP with truth-point benchmarking -# Metadata -N = 3069 -T = 31 -S = 3 -P = 4 +# 1.1. true_point benchmarking +true_pointest <- as_tibble(read.csv("./data/DMSep_validation_trueparam.csv")) +# 1.2. true_distribution +summ <- summarise_draws(gpImputeDMSepFit(1:99), "mean", "sd") +#true_sample <- sampling_res summ <- summarise_draws(true_sample, "mean", "sd") +true_p21_hp <- summ[4,c(2,3)] +true_p31_hp <- summ[5,c(2,3)] +true_rate_hp <- summ[c(1,2,3),c(2,3)] +############## +# truth-point benchmarking +############## +true_hp <- list(true_p21_hp, true_p31_hp, true_rate_hp) p21 <-true$x[true$X== "p21"] #0.1 # p31 <- true$x[true$X== "p31"] #0.2 # rate <- rep(NA, S); for (j in 1:S) rate[j] <- true$x[true$X== sprintf("rate[%s]", j)] true_pars <-list(p21, p31, rate) - -generator <- function(){ +point_generator <- function(){ function(iter, true_pars){ # Metadata N = 3069 @@ -40,24 +70,98 @@ generator <- function(){ Mnt = array(c(1, 0, 0, p21, 1- p21, 0, p31, 1-p31,0), dim=c(S,S)) # Deterioration tmp_p <- array(rep(0, S * S), dim=c(S,S)) - tmp_p[1,1] = exp(-rate[1]- rate[2]); - tmp_p[2,1] = rate[1] * exp(-rate[3]) * (1-exp(-(rate[1]+ rate[2] - rate[3]))) / (rate[1]+ rate[2] - rate[3]); - tmp_p[3,1] = exp(-rate[3]); + tmp_p[1,1] = exp(-rate[1]- rate[2]) + tmp_p[2,1] = rate[1] * exp(-rate[3]) * (1-exp(-(rate[1]+ rate[2] - rate[3]))) / (rate[1]+ rate[2] - rate[3]) + tmp_p[3,1] = exp(-rate[3]) Dtr <- array(c(tmp_p[1,1], tmp_p[2,1], 1- tmp_p[1,1], 0, tmp_p[3,1], 1 - tmp_p[3,1], 0,0,1), dim=c(S, S)) latent_states[1,] = Dtr %*% init_state_vec; for (t in 2:T){ latent_states[t,] = (Dtr %*% Mnt) %*% latent_states[t-1,]; } - list( - generated = lapply(seq(1:length(obs2time)), function(n){sample(c(1,2,3), 1, prob = latent_states[obs2time[n],])}), - parameters = list( - p21 = p21, - p31 = p31, - rate = rate + list( + generated = lapply(seq(1:length(obs2time)), function(n){sample(c(1,2,3), 1, prob = latent_states[obs2time[n],])}), + parameters = list( + p21 = p21, + p31 = p31, + rate = rate + ) ) - ) } } +############## +# truth-dist. benchmarking +############## +true_hp <- lapply(seq(1:length(true_hp)), function(x){as.numeric(unlist(true_hp[[x]]))}) +# 2. data simulator +# DGP with truth-point benchmarking +# Metadata +N = 3069 +T = 31 +S = 3 +P = 4 +dist2samp <- function(true_hp){ + estBetaParams <- function(mu, var) { + alpha <- ((1 - mu) / var - 1 / mu) * mu ^ 2 + beta <- alpha * (1 / mu - 1) + return(params = list(alpha = alpha, beta = beta)) + } + p21 = rnorm(1, true_hp[[1]][1], true_hp[[1]][2]) #rnorm(1, true_hp[[1]][1], true_hp[[1]][2]) #rbeta(1,p21_hp$alpha, p21_hp$beta) + p31 = rnorm(1, true_hp[[2]][1], true_hp[[2]][2]) #rbeta(1, p31_hp$alpha, p31_hp$beta) + for (i in 1:3){ + #ratet_hp <- gammaParamsConvert(mean=true_hp[[3]][1],sd=true_hp[[3]][2]) + rate[i] = rnorm(1,true_hp[[3]][1], true_hp[[3]][2]/3) #rgamma(1, ratet_hp$shape, ratet_hp$scale) + } + return(list(p21, p31, rate)) +} +#lapply(1:5, function(x) {dist2samp(true_hp) +true_samples <- list() +for (i in 1:SBC_N){ + true_samples <- c(tmp, list(dist2samp(true_hp))) +} +custom_SBC_generator() +generator_dist <- function(true_hp){ + print(true_hp) + # Metadata + N = 3069 + T = 31 + S = 3 + P = 4 + obs2time = complete(generateMice(), 1)$age_ind + initial_state = 1 + init_state_vec = rep(0, S); + init_state_vec[initial_state] = 1 + latent_states = array(NA, dim=c(T,S)) + for (i in SBC_N){ + p21 = true_hp[[1]] #rbeta(1,p21_hp$alpha, p21_hp$beta) + p31 = rnorm(1, true_hp[[2]][1], true_hp[[2]][2] ) #rbeta(1, p31_hp$alpha, p31_hp$beta) + for (i in 1:3){ + #ratet_hp <- gammaParamsConvert(mean=true_hp[[3]][1],sd=true_hp[[3]][2]) + rate[i] = true_hp[[3]][i] #rnorm(1,true_hp[[3]][1], true_hp[[3]][2]/3) #rgamma(1, ratet_hp$shape, ratet_hp$scale) + } + # Maintenance + Mnt = array(c(1, 0, 0, p21, 1- p21, 0, p31, 1-p31,0), dim=c(S,S)) + # Deterioration + tmp_p <- array(rep(0, S * S), dim=c(S,S)) + tmp_p[1,1] = exp(-rate[1]- rate[2]) + tmp_p[2,1] = rate[1] * exp(-rate[3]) * (1-exp(-(rate[1]+ rate[2] - rate[3]))) / (rate[1]+ rate[2] - rate[3]) + tmp_p[3,1] = exp(-rate[3]) + Dtr <- array(c(tmp_p[1,1], tmp_p[2,1], 1- tmp_p[1,1], 0, tmp_p[3,1], 1 - tmp_p[3,1], 0,0,1), dim=c(S, S)) + latent_states[1,] = Dtr %*% init_state_vec + for (t in 2:T){ + latent_states[t,] = (Dtr %*% Mnt) %*% latent_states[t-1,] + } + } + #p21_hp <- estBetaParams(true_hp[[1]][1],(true_hp[[1]][2])^2) this gives all 1 + #p31_hp <- estBetaParams(true_hp[[2]][1],(true_hp[[2]][2])^2) + list( + generated = lapply(seq(1:length(obs2time)), function(n){sample(c(1,2,3), 1, prob = latent_states[obs2time[n],])}), + parameters = list( + p21 = p21, + p31 = p31, + rate = rate + ) + ) + } # 3. posterior simulator: prior and data simulator embedded in stan with supplied data # 3-1. prior and data simulator @@ -70,11 +174,32 @@ obs2time <- complete(generateMice(), 1)$age_ind # data template only states <- rep(3, N) # data template initial_state <- 1 stan_data <- list(N= N,T = max(obs2time), S = 3, P = 4, states=states, obs2time=obs2time, initial_state=initial_state) -SBC_N = 1 -SBC_M = 500 # 40 -workflow <- SBCWorkflow$new(DMSep, generator()) -workflow$simulate(SBC_N, true_pars) +SBC_N = 3 +SBC_M = 20 # 40 +# truth-point +# workflow <- SBCWorkflow$new(DMSep, generator()) +# workflow$simulate(SBC_N, true_pointest) +# truth-dist +workflow <- SBCWorkflow$new(DMSep, generator_dist()) +workflow$simulate(SBC_N, dist2samp(true_hp)) stan_data$states <- unlist(posterior::draws_of(posterior::subset_draws(workflow$simulated_y, variable="y")$y)) write.csv(stan_data$states, file = "sim_y.csv") summary(stan_data$states) sampling_res<-DMSep$sample(data = stan_data,iter_warmup =1000, iter_sampling = 1000, parallel_chains = 4) + +workflow$fit_model(sample_iterations = SBC_M, warmup_iterations = 500, stan_data) +prior <- workflow$prior_samples +post <- workflow$posterior_samples +workflow$calculate_rank() +plot_ecdf(workflow, var = "theta") +plot_ecdf_diff(workflow, var="theta") + + +######## +cmdgenerator_func <- custom_SBC_generator(generator_dist, true_samples) +datasets_func <- generate_datasets(cmdgenerator_func, SBC_N) +backend <- cmdstan_sample_SBC_backend(DMSep, iter_warmup = 200, iter_sampling = 200) +results <- compute_results(datasets, backend) +plot_ecdf_diff(results) +######## + diff --git a/R/2_Theta_bar_Y/DtrMngSep/DMsep_5param_test.R b/R/2_Theta_bar_Y/DtrMngSep/DMsep_5param_test.R index 90d2421..164f14d 100644 --- a/R/2_Theta_bar_Y/DtrMngSep/DMsep_5param_test.R +++ b/R/2_Theta_bar_Y/DtrMngSep/DMsep_5param_test.R @@ -1,48 +1,47 @@ -source(file.path(getwd(), "R/1_Y_bar_y/impute/mice_imputation.R")) -scriptDir <- getwd() -set.seed(210106) library(rstan) library(cmdstanr) library(ggplot2) library(scales) - -#cmdstan: model_DMsep <-stan_m(file = (file.path(getwd(), "R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan"))) -model_DMsep <-stan_model(file = (file.path(getwd(), "R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan"))) - -mice_imp <- generateMice() -imputed_data<- complete(mice_imp, 1) - -imputed_data_gp <- read.csv("data/y_pred_5var.csv")[,-1] -imputed_data$y_data <- unlist(c(imputed_data_gp)) -#################################### DM_sep -# original policy (wihtout pm) -n_state = 3 -initial_state = 1 - -generate_state_matrix <- function(data, n){ - #state <- cut(data, breaks=c(0, 80, 160, max(data)), labels=1:n, include.lowest = TRUE) - state <- cut(data, breaks=quantile(data,c(0,1/3,2/3,1)), labels=1:n, include.lowest = TRUE) - state<-as.numeric(state) - matrix(state,nrow=31) +source(file.path(getwd(), "R/1_Y_bar_y/impute/mice_imputation.R")) +set.seed(210106) +gpImputeDMSepFit <- function(train_ship_ind){ + #cmdstan: model_DMsep <-stan_m(file = (file.path(getwd(), "R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan"))) + model_DMsep <-stan_model(file = (file.path(getwd(), "R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan"))) + mice_imp <- generateMice() + imputed_data<- complete(mice_imp, 1) + imputed_data_gp <- read.csv("data/y_pred_5var.csv")[,-1] + imputed_data$y_data <- unlist(c(imputed_data_gp)) + #################################### DM_sep + # original policy (wihtout pm) + n_state = 3 + initial_state = 1 + generate_state_matrix <- function(data, n){ + #state <- cut(data, breaks=c(0, 80, 160, max(data)), labels=1:n, include.lowest = TRUE) + state <- cut(data, breaks=quantile(data,c(0,1/3,2/3,1)), labels=1:n, include.lowest = TRUE) + state<-as.numeric(state) + matrix(state,nrow=31) + } + state_matrix <- generate_state_matrix(imputed_data$y_data, n_state) + states <- as.vector(t(state_matrix)) + iter=2000 + MSE_df<-data.frame(index=rep(0,iter),p=rep(0,iter),q=rep(0,iter),train_MSE=rep(0,iter),test_MSE=rep(0,iter)) + rate_array<-data.frame(period=c(),index=c(),rate=c()) + D_array <- array(0, dim=c(iter,4,3,3)) + ship_ind_df<-matrix(0,nrow=iter,ncol=5) + train_ind=c(sapply(train_ship_ind,function(x) (x-1)*31+(1:31))) + stan_data <- list(N= length(train_ind),T = max(imputed_data$age_ind[train_ind]), S = 3, P = 4, states=states[train_ind], obs2time=imputed_data$age_ind[train_ind], initial_state=initial_state) + sampling_res<-sampling(model_DMsep,stan_data, iter = 2000, cores=4) + return (sampling_res) } -state_matrix <- generate_state_matrix(imputed_data$y_data, n_state) -states <- as.vector(t(state_matrix)) - -iter=2000 -MSE_df<-data.frame(index=rep(0,iter),p=rep(0,iter),q=rep(0,iter),train_MSE=rep(0,iter),test_MSE=rep(0,iter)) -rate_array<-data.frame(period=c(),index=c(),rate=c()) -D_array <- array(0, dim=c(iter,4,3,3)) -ship_ind_df<-matrix(0,nrow=iter,ncol=5) - -test_ship_ind= c(17,20,24,77,82) #sort(sample(1:99,5)) #c(17,20,24,77,82) +test_ship_ind= c(17,20,24,77,82) #sort(sample(1:99,5)) = c(17,20,24,77,82) ship_ind_df[1,]=test_ship_ind test_ind=c(sapply(test_ship_ind,function(x) (x-1)*31+(1:31))) - train_data <- states[-test_ind] test_data <- states[test_ind] -stan_data <- list(N= length(train_data),T = max(imputed_data$age_ind[-test_ind]), S = 3, P = 4, states=train_data, obs2time=imputed_data$age_ind[-test_ind], initial_state=initial_state) -sampling_res<-sampling(model_DMsep,stan_data, iter = 2000, cores=4) +#sort(sample(1:99,5)) #c(17,20,24,77,82) +gpImputeDMSepFit(c(17,20,24,77,82)) + res_df<-as.data.frame(sampling_res) sample_mean<-apply(res_df,2,mean) #write.csv(sample_mean, "./data/DMSep_validation_trueparam.csv") diff --git a/R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan b/R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan index 71bcc51..74702e5 100644 --- a/R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan +++ b/R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan @@ -28,7 +28,9 @@ transformed parameters { matrix[S, S] Dtr; // Deterioration matrix[S, S] Mnt; // Maintenance simplex[S] latent_states[T]; - matrix[S, S] tmp_p; + real tmp_p11; + real tmp_p21; + real tmp_p31; // Maintenance // is left stoch. matrix, transpose of eq.14 from the paper Mnt = [[1, p21, p31], @@ -36,12 +38,12 @@ transformed parameters { [0, 0, 0]]; // Deterioration by period // is left stoch. matrix, transpose of eq.13 from the paper - tmp_p[1,1] = exp(-rate[1]- rate[2]); - tmp_p[2,1] = rate[1] * exp(-rate[3]) * (1-exp(-(rate[1]+ rate[2] - rate[3]))) / (rate[1]+ rate[2] - rate[3]); - tmp_p[3,1] = exp(-rate[3]); - Dtr = [[tmp_p[1,1], 0, 0], - [tmp_p[2,1], tmp_p[3,1], 0], - [1 - tmp_p[1,1] - tmp_p[2,1], 1 - tmp_p[3,1], 1]]; + tmp_p11 = exp(-rate[1]- rate[2]); + tmp_p21 = rate[1] * exp(-rate[3]) * (1-exp(-(rate[1]+ rate[2] - rate[3]))) / (rate[1]+ rate[2] - rate[3]); + tmp_p31 = exp(-rate[3]); + Dtr = [[tmp_p11, 0, 0], + [tmp_p21, tmp_p31, 0], + [1 - tmp_p11 - tmp_p21, 1 - tmp_p31, 1]]; // Inhomogenous Dtr latent_states[1] = Dtr * initial; for (t in 2:T){ From f9fc097167720b946efbdfa882b45b32841b4112 Mon Sep 17 00:00:00 2001 From: hyunjimoon Date: Wed, 11 Aug 2021 10:59:39 +0900 Subject: [PATCH 2/5] Dist. three times SBC running for DMSep --- R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R | 207 ++++-------------- .../DtrMngSep/models/DMSep/DMSep.stan | 8 +- 2 files changed, 52 insertions(+), 163 deletions(-) diff --git a/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R b/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R index 4601924..694c488 100644 --- a/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R +++ b/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R @@ -3,7 +3,7 @@ library(SBC) library(mice) library(cmdstanr) library(dplyr) -library("ConnMatTools") +library(ConnMatTools) source(file.path(getwd(), "R/1_Y_bar_y/impute/engine_fail_imputation.R")) source("R/utils/functions.r") source("R/2_Theta_bar_Y/DtrMngSep/DMsep_5param_test.R") @@ -18,7 +18,6 @@ imputed_data$y_data <- unlist(c(imputed_data_gp)) n_state = 3 initial_state = 1 generate_state_matrix <- function(data, n){ - #state <- cut(data, breaks=c(0, 80, 160, max(data)), labels=1:n, include.lowest = TRUE) state <- cut(data, breaks=quantile(data,c(0,1/3,2/3,1)), labels=1:n, include.lowest = TRUE) state<-as.numeric(state) matrix(state,nrow=31) @@ -31,175 +30,59 @@ MSE_df<-data.frame(index=rep(0,iter),p=rep(0,iter),q=rep(0,iter),train_MSE=rep(0 rate_array<-data.frame(period=c(),index=c(),rate=c()) D_array <- array(0, dim=c(iter,4,3,3)) ship_ind_df<-matrix(0,nrow=iter,ncol=5) -# Three simulators for prior, data, posterior + +sampling_res <- gpImputeDMSepFit(1:99) +true_prival <- subset_draws(as_draws_rvars(sampling_res), chain = 1, iteration = 1:10) # 1. prior simulator -# 1.1. true_point benchmarking -true_pointest <- as_tibble(read.csv("./data/DMSep_validation_trueparam.csv")) -# 1.2. true_distribution -summ <- summarise_draws(gpImputeDMSepFit(1:99), "mean", "sd") -#true_sample <- sampling_res summ <- summarise_draws(true_sample, "mean", "sd") -true_p21_hp <- summ[4,c(2,3)] -true_p31_hp <- summ[5,c(2,3)] -true_rate_hp <- summ[c(1,2,3),c(2,3)] +# Three ways for prior input: truth-point bencmarking, hyperparameter-based truth-dist, sample-based truth-dist, +# The first only simulate with one set of value of parameter values while the second and the third simulate with +# different set of prior values. The second is when the modeler only knows the hyperparmeter of prior values while +# the third is when the modeler have exact n_datasets pairs of prior value in hand. + ############## -# truth-point benchmarking +# truth-dist. with custom priorval ############## -true_hp <- list(true_p21_hp, true_p31_hp, true_rate_hp) -p21 <-true$x[true$X== "p21"] #0.1 # -p31 <- true$x[true$X== "p31"] #0.2 # -rate <- rep(NA, S); -for (j in 1:S) rate[j] <- true$x[true$X== sprintf("rate[%s]", j)] -true_pars <-list(p21, p31, rate) -point_generator <- function(){ - function(iter, true_pars){ - # Metadata - N = 3069 - T = 31 - S = 3 - P = 4 - obs2time = complete(generateMice(), 1)$age_ind - initial_state = 1 - init_state_vec = rep(0, S); - init_state_vec[initial_state] = 1 - latent_states = array(NA, dim=c(T,S)) - # Hyperparameter -> receive as input - p21 = true_pars[[1]] - p31 = true_pars[[2]] - rate = true_pars[[3]] +custom_nprior_generator <- function(p21, p31, rate){ + n_datasets <- length(p21) + generated <- list() + N = 3069 + T = 31 + S = 3 + P = 4 + obs2time = complete(generateMice(), 1)$age_ind + initial_state = 1 + init_state_vec = rep(0, S); + init_state_vec[initial_state] = 1 + latent_states = array(NA, dim=c(T,S)) + gen_states = array(NA, N) + rate_vec <- rep(NA, 3) + for(iter in 1:n_datasets){ + rate_i = rate[iter,] # Maintenance - Mnt = array(c(1, 0, 0, p21, 1- p21, 0, p31, 1-p31,0), dim=c(S,S)) + Mnt = array(c(1, 0, 0, p21[iter], 1- p21[iter], 0, p31[iter], 1-p31[iter],0), dim=c(S,S)) # Deterioration - tmp_p <- array(rep(0, S * S), dim=c(S,S)) - tmp_p[1,1] = exp(-rate[1]- rate[2]) - tmp_p[2,1] = rate[1] * exp(-rate[3]) * (1-exp(-(rate[1]+ rate[2] - rate[3]))) / (rate[1]+ rate[2] - rate[3]) - tmp_p[3,1] = exp(-rate[3]) + tmp_p <- array(rep(NA, S * S), dim=c(S,S)) + tmp_p[1,1] <- exp(-rate_i[1]- rate_i[2]) + tmp_p[2,1] <- rate_i[1] * exp(-rate_i[3]) * (1-exp(-(rate_i[1] + rate_i[2] - rate_i[3]))) / (rate_i[1] + rate_i[2] - rate_i[3]) + tmp_p[3,1] <- exp(-rate_i[3]) Dtr <- array(c(tmp_p[1,1], tmp_p[2,1], 1- tmp_p[1,1], 0, tmp_p[3,1], 1 - tmp_p[3,1], 0,0,1), dim=c(S, S)) - latent_states[1,] = Dtr %*% init_state_vec; + latent_states[1,] <- Dtr %*% init_state_vec for (t in 2:T){ - latent_states[t,] = (Dtr %*% Mnt) %*% latent_states[t-1,]; + latent_states[t,] <- (Dtr %*% Mnt) %*% latent_states[t-1,] } - list( - generated = lapply(seq(1:length(obs2time)), function(n){sample(c(1,2,3), 1, prob = latent_states[obs2time[n],])}), - parameters = list( - p21 = p21, - p31 = p31, - rate = rate - ) - ) + gen_states <- unlist(lapply(seq(1:length(obs2time)), function(n){sample(c(1,2,3), 1, prob = latent_states[obs2time[n],])})) + generated[[iter]] <- list(N = N, T = T, S = S, P = P, states = gen_states, obs2time = obs2time, initial_state = initial_state) } + SBC_datasets( + parameters = posterior::as_draws_matrix(posterior::draws_rvars(p21 = rvar(p21), p31 = rvar(p31), rate = rvar(rate))), + generated <- generated + ) } -############## -# truth-dist. benchmarking -############## -true_hp <- lapply(seq(1:length(true_hp)), function(x){as.numeric(unlist(true_hp[[x]]))}) -# 2. data simulator -# DGP with truth-point benchmarking -# Metadata -N = 3069 -T = 31 -S = 3 -P = 4 -dist2samp <- function(true_hp){ - estBetaParams <- function(mu, var) { - alpha <- ((1 - mu) / var - 1 / mu) * mu ^ 2 - beta <- alpha * (1 / mu - 1) - return(params = list(alpha = alpha, beta = beta)) - } - p21 = rnorm(1, true_hp[[1]][1], true_hp[[1]][2]) #rnorm(1, true_hp[[1]][1], true_hp[[1]][2]) #rbeta(1,p21_hp$alpha, p21_hp$beta) - p31 = rnorm(1, true_hp[[2]][1], true_hp[[2]][2]) #rbeta(1, p31_hp$alpha, p31_hp$beta) - for (i in 1:3){ - #ratet_hp <- gammaParamsConvert(mean=true_hp[[3]][1],sd=true_hp[[3]][2]) - rate[i] = rnorm(1,true_hp[[3]][1], true_hp[[3]][2]/3) #rgamma(1, ratet_hp$shape, ratet_hp$scale) - } - return(list(p21, p31, rate)) -} -#lapply(1:5, function(x) {dist2samp(true_hp) -true_samples <- list() -for (i in 1:SBC_N){ - true_samples <- c(tmp, list(dist2samp(true_hp))) -} -custom_SBC_generator() -generator_dist <- function(true_hp){ - print(true_hp) - # Metadata - N = 3069 - T = 31 - S = 3 - P = 4 - obs2time = complete(generateMice(), 1)$age_ind - initial_state = 1 - init_state_vec = rep(0, S); - init_state_vec[initial_state] = 1 - latent_states = array(NA, dim=c(T,S)) - for (i in SBC_N){ - p21 = true_hp[[1]] #rbeta(1,p21_hp$alpha, p21_hp$beta) - p31 = rnorm(1, true_hp[[2]][1], true_hp[[2]][2] ) #rbeta(1, p31_hp$alpha, p31_hp$beta) - for (i in 1:3){ - #ratet_hp <- gammaParamsConvert(mean=true_hp[[3]][1],sd=true_hp[[3]][2]) - rate[i] = true_hp[[3]][i] #rnorm(1,true_hp[[3]][1], true_hp[[3]][2]/3) #rgamma(1, ratet_hp$shape, ratet_hp$scale) - } - # Maintenance - Mnt = array(c(1, 0, 0, p21, 1- p21, 0, p31, 1-p31,0), dim=c(S,S)) - # Deterioration - tmp_p <- array(rep(0, S * S), dim=c(S,S)) - tmp_p[1,1] = exp(-rate[1]- rate[2]) - tmp_p[2,1] = rate[1] * exp(-rate[3]) * (1-exp(-(rate[1]+ rate[2] - rate[3]))) / (rate[1]+ rate[2] - rate[3]) - tmp_p[3,1] = exp(-rate[3]) - Dtr <- array(c(tmp_p[1,1], tmp_p[2,1], 1- tmp_p[1,1], 0, tmp_p[3,1], 1 - tmp_p[3,1], 0,0,1), dim=c(S, S)) - latent_states[1,] = Dtr %*% init_state_vec - for (t in 2:T){ - latent_states[t,] = (Dtr %*% Mnt) %*% latent_states[t-1,] - } - } - #p21_hp <- estBetaParams(true_hp[[1]][1],(true_hp[[1]][2])^2) this gives all 1 - #p31_hp <- estBetaParams(true_hp[[2]][1],(true_hp[[2]][2])^2) - list( - generated = lapply(seq(1:length(obs2time)), function(n){sample(c(1,2,3), 1, prob = latent_states[obs2time[n],])}), - parameters = list( - p21 = p21, - p31 = p31, - rate = rate - ) - ) - } - -# 3. posterior simulator: prior and data simulator embedded in stan with supplied data -# 3-1. prior and data simulator -modelName = "DMSep" -Dir <- set_get_Dir(modelName, "R/2_Theta_bar_Y/DtrMngSep") -DMSep = cmdstanr::cmdstan_model(Dir$file) - -# 3-2. simulated y data template with init as real y -obs2time <- complete(generateMice(), 1)$age_ind # data template only -states <- rep(3, N) # data template -initial_state <- 1 -stan_data <- list(N= N,T = max(obs2time), S = 3, P = 4, states=states, obs2time=obs2time, initial_state=initial_state) -SBC_N = 3 -SBC_M = 20 # 40 -# truth-point -# workflow <- SBCWorkflow$new(DMSep, generator()) -# workflow$simulate(SBC_N, true_pointest) -# truth-dist -workflow <- SBCWorkflow$new(DMSep, generator_dist()) -workflow$simulate(SBC_N, dist2samp(true_hp)) -stan_data$states <- unlist(posterior::draws_of(posterior::subset_draws(workflow$simulated_y, variable="y")$y)) -write.csv(stan_data$states, file = "sim_y.csv") -summary(stan_data$states) -sampling_res<-DMSep$sample(data = stan_data,iter_warmup =1000, iter_sampling = 1000, parallel_chains = 4) - -workflow$fit_model(sample_iterations = SBC_M, warmup_iterations = 500, stan_data) -prior <- workflow$prior_samples -post <- workflow$posterior_samples -workflow$calculate_rank() -plot_ecdf(workflow, var = "theta") -plot_ecdf_diff(workflow, var="theta") - - -######## -cmdgenerator_func <- custom_SBC_generator(generator_dist, true_samples) -datasets_func <- generate_datasets(cmdgenerator_func, SBC_N) -backend <- cmdstan_sample_SBC_backend(DMSep, iter_warmup = 200, iter_sampling = 200) -results <- compute_results(datasets, backend) +prival <- list(p21 = sampling_res_rvar$p21, p31 = sampling_res_rvar$p31, rate = sampling_res_rvar$rate) +pri_val <- list(p21 = c(0.8, 0.7,0.81,0.8, 0.7,0.81,0.8, 0.7,0.81), p31 = c(0.8, 0.7,0.81,0.8, 0.7,0.81,0.8, 0.7,0.81), + rate =matrix(c(.66, .27, .65, .7, .3,.55, 0.69, .26, .66, .66, .27, .65, .7, .3,.55, 0.69, .26, .66, .66, .27, .65, .7, .3,.55, 0.69, .26, .66), nrow = 9, ncol = 3, byrow = TRUE)) +datasets <- custom_nprior_generator(p21 = c(0.8, 0.7,0.81,0.8, 0.7,0.81,0.8, 0.7,0.81), p31 = c(0.8, 0.7,0.81,0.8, 0.7,0.81,0.8, 0.7,0.81), rate =matrix(c(.66, .27, .65, .7, .3,.55, 0.69, .26, .66, .66, .27, .65, .7, .3,.55, 0.69, .26, .66, .66, .27, .65, .7, .3,.55, 0.69, .26, .66), nrow = 9, ncol = 3, byrow = TRUE)) +datasets <- custom_nprior_generator(prival) plot_ecdf_diff(results) -######## + diff --git a/R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan b/R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan index 74702e5..61325f3 100644 --- a/R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan +++ b/R/2_Theta_bar_Y/DtrMngSep/models/DMSep/DMSep.stan @@ -44,7 +44,7 @@ transformed parameters { Dtr = [[tmp_p11, 0, 0], [tmp_p21, tmp_p31, 0], [1 - tmp_p11 - tmp_p21, 1 - tmp_p31, 1]]; - // Inhomogenous Dtr + // (In) or homogenous Dtr latent_states[1] = Dtr * initial; for (t in 2:T){ latent_states[t] = (Dtr * Mnt) *latent_states[t-1]; //matrix_power((Dtr * Mnt), (t-1)) * initial; @@ -56,3 +56,9 @@ model { states[n] ~ categorical(latent_states[obs2time[n]]); } } +generated quantities{ +int gen_states[N]; + for (n in 1:N){ + gen_states[n] = categorical_rng(latent_states[obs2time[n]]); + } +} From 8f65b1815c23be0c124b4941f68e8d3c7495ef0f Mon Sep 17 00:00:00 2001 From: hyunjimoon Date: Wed, 11 Aug 2021 19:33:15 +0900 Subject: [PATCH 3/5] success sbc 30 times --- R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R | 37 +++++++++++++-------------- 1 file changed, 18 insertions(+), 19 deletions(-) diff --git a/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R b/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R index 694c488..5af661d 100644 --- a/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R +++ b/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R @@ -3,6 +3,7 @@ library(SBC) library(mice) library(cmdstanr) library(dplyr) +library(posterior) library(ConnMatTools) source(file.path(getwd(), "R/1_Y_bar_y/impute/engine_fail_imputation.R")) source("R/utils/functions.r") @@ -32,8 +33,7 @@ D_array <- array(0, dim=c(iter,4,3,3)) ship_ind_df<-matrix(0,nrow=iter,ncol=5) sampling_res <- gpImputeDMSepFit(1:99) -true_prival <- subset_draws(as_draws_rvars(sampling_res), chain = 1, iteration = 1:10) -# 1. prior simulator +true_prival <- subset_draws(as_draws_rvars(sampling_res), chain = 1, iteration = seq(1, 900, 30)) # Three ways for prior input: truth-point bencmarking, hyperparameter-based truth-dist, sample-based truth-dist, # The first only simulate with one set of value of parameter values while the second and the third simulate with # different set of prior values. The second is when the modeler only knows the hyperparmeter of prior values while @@ -42,8 +42,8 @@ true_prival <- subset_draws(as_draws_rvars(sampling_res), chain = 1, iteration = ############## # truth-dist. with custom priorval ############## -custom_nprior_generator <- function(p21, p31, rate){ - n_datasets <- length(p21) +custom_nprior_generator <- function(true_prival){ + n_datasets <- niterations(true_prival) generated <- list() N = 3069 T = 31 @@ -53,18 +53,21 @@ custom_nprior_generator <- function(p21, p31, rate){ initial_state = 1 init_state_vec = rep(0, S); init_state_vec[initial_state] = 1 - latent_states = array(NA, dim=c(T,S)) + latent_states = matrix(rep(NA, T*S),nrow = T, ncol = S) gen_states = array(NA, N) rate_vec <- rep(NA, 3) for(iter in 1:n_datasets){ - rate_i = rate[iter,] + true_prival_i <- subset_draws(true_prival, iteration = iter) + rate_i <- draws_of(true_prival_i$rate) + p21_i <- draws_of(true_prival_i$p21) + p31_i <- draws_of(true_prival_i$p31) # Maintenance - Mnt = array(c(1, 0, 0, p21[iter], 1- p21[iter], 0, p31[iter], 1-p31[iter],0), dim=c(S,S)) + Mnt = array(c(1, 0, 0, p21_i, 1- p21_i, 0, p31_i, 1-p31_i,0), dim=c(S,S)) # Deterioration - tmp_p <- array(rep(NA, S * S), dim=c(S,S)) - tmp_p[1,1] <- exp(-rate_i[1]- rate_i[2]) - tmp_p[2,1] <- rate_i[1] * exp(-rate_i[3]) * (1-exp(-(rate_i[1] + rate_i[2] - rate_i[3]))) / (rate_i[1] + rate_i[2] - rate_i[3]) - tmp_p[3,1] <- exp(-rate_i[3]) + tmp_p <- array(rep(0, S * S), dim=c(S,S)) + tmp_p[1,1] <- exp(- rate_i[,1]- rate_i[,2]) + tmp_p[2,1] <- rate_i[,1] * exp(- rate_i[,3]) * (1-exp(-( rate_i[,1] + rate_i[,2] - rate_i[,3]))) / ( rate_i[,1] + rate_i[,2] - rate_i[,3]) + tmp_p[3,1] <- exp(- rate_i[,3]) Dtr <- array(c(tmp_p[1,1], tmp_p[2,1], 1- tmp_p[1,1], 0, tmp_p[3,1], 1 - tmp_p[3,1], 0,0,1), dim=c(S, S)) latent_states[1,] <- Dtr %*% init_state_vec for (t in 2:T){ @@ -74,15 +77,11 @@ custom_nprior_generator <- function(p21, p31, rate){ generated[[iter]] <- list(N = N, T = T, S = S, P = P, states = gen_states, obs2time = obs2time, initial_state = initial_state) } SBC_datasets( - parameters = posterior::as_draws_matrix(posterior::draws_rvars(p21 = rvar(p21), p31 = rvar(p31), rate = rvar(rate))), + parameters = posterior::as_draws_matrix(posterior::draws_rvars(p21 = true_prival$p21, p31 = true_prival$p31, rate = true_prival$rate)), generated <- generated ) } -prival <- list(p21 = sampling_res_rvar$p21, p31 = sampling_res_rvar$p31, rate = sampling_res_rvar$rate) -pri_val <- list(p21 = c(0.8, 0.7,0.81,0.8, 0.7,0.81,0.8, 0.7,0.81), p31 = c(0.8, 0.7,0.81,0.8, 0.7,0.81,0.8, 0.7,0.81), - rate =matrix(c(.66, .27, .65, .7, .3,.55, 0.69, .26, .66, .66, .27, .65, .7, .3,.55, 0.69, .26, .66, .66, .27, .65, .7, .3,.55, 0.69, .26, .66), nrow = 9, ncol = 3, byrow = TRUE)) -datasets <- custom_nprior_generator(p21 = c(0.8, 0.7,0.81,0.8, 0.7,0.81,0.8, 0.7,0.81), p31 = c(0.8, 0.7,0.81,0.8, 0.7,0.81,0.8, 0.7,0.81), rate =matrix(c(.66, .27, .65, .7, .3,.55, 0.69, .26, .66, .66, .27, .65, .7, .3,.55, 0.69, .26, .66, .66, .27, .65, .7, .3,.55, 0.69, .26, .66), nrow = 9, ncol = 3, byrow = TRUE)) -datasets <- custom_nprior_generator(prival) +datasets <- custom_nprior_generator(true_prival) +backend <- cmdstan_sample_SBC_backend(DMSep, iter_warmup = 200, iter_sampling = 200) +results <- compute_results(datasets, backend) plot_ecdf_diff(results) - - From 4e5f6e59cc0e0b10bd9ed83b8b3e3557a4ba48a0 Mon Sep 17 00:00:00 2001 From: hyunjimoon Date: Thu, 12 Aug 2021 10:28:37 +0900 Subject: [PATCH 4/5] DMSep_SBC bug fix --- R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R | 11 +- data/DMSep_validation_trueparam.csv | 467 +++++------------- .../Halevi06_Book_TheoryOfCryptography.pdf | Bin .../KoreaDataPlatform.pdf | Bin .../Lai20_StatModStochOptFinTechInvSci.pdf | Bin 330590 -> 396044 bytes .../ML on Ecrypted Data copy.pdf | Bin .../Micciancio20_CryptoLN.pdf | Bin ...OLIO CREDIT RISK IN THE t-COPULA MODEL.pdf | Bin 0 -> 182252 bytes .../Glasserman15_RiskWeightsDesign.pdf | Bin 0 -> 1109520 bytes .../failure_count.csv | 0 .../sc21_notebook.Rmd | 0 .../y_pred_5var.csv | 0 12 files changed, 133 insertions(+), 345 deletions(-) rename docs/{DefenseSystem => DataPlatformTech}/Halevi06_Book_TheoryOfCryptography.pdf (100%) rename docs/{DefenseSystem => DataPlatformTech}/KoreaDataPlatform.pdf (100%) rename docs/{DefenseSystem => DataPlatformTech}/Lai20_StatModStochOptFinTechInvSci.pdf (83%) rename docs/{DefenseSystem => DataPlatformTech}/ML on Ecrypted Data copy.pdf (100%) rename docs/{DefenseSystem => DataPlatformTech}/Micciancio20_CryptoLN.pdf (100%) create mode 100644 docs/SystemRisk/FAST SIMULATION FOR MULTIFACTOR PORTFOLIO CREDIT RISK IN THE t-COPULA MODEL.pdf create mode 100644 docs/SystemRisk/Glasserman15_RiskWeightsDesign.pdf rename vignettes/{stancon21 => DeterMaintSep}/failure_count.csv (100%) rename vignettes/{stancon21 => DeterMaintSep}/sc21_notebook.Rmd (100%) rename vignettes/{stancon21 => DeterMaintSep}/y_pred_5var.csv (100%) diff --git a/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R b/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R index 5af661d..7b96c3f 100644 --- a/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R +++ b/R/2_Theta_bar_Y/DtrMngSep/DMSep_SBC.R @@ -33,7 +33,8 @@ D_array <- array(0, dim=c(iter,4,3,3)) ship_ind_df<-matrix(0,nrow=iter,ncol=5) sampling_res <- gpImputeDMSepFit(1:99) -true_prival <- subset_draws(as_draws_rvars(sampling_res), chain = 1, iteration = seq(1, 900, 30)) +saveRDS(sampling_res, "sample_genstate.RDS") +true_prival <- subset_draws(as_draws_rvars(sampling_res), chain = 1, iteration = seq(1, 981, by = 20)) # Three ways for prior input: truth-point bencmarking, hyperparameter-based truth-dist, sample-based truth-dist, # The first only simulate with one set of value of parameter values while the second and the third simulate with # different set of prior values. The second is when the modeler only knows the hyperparmeter of prior values while @@ -68,7 +69,7 @@ custom_nprior_generator <- function(true_prival){ tmp_p[1,1] <- exp(- rate_i[,1]- rate_i[,2]) tmp_p[2,1] <- rate_i[,1] * exp(- rate_i[,3]) * (1-exp(-( rate_i[,1] + rate_i[,2] - rate_i[,3]))) / ( rate_i[,1] + rate_i[,2] - rate_i[,3]) tmp_p[3,1] <- exp(- rate_i[,3]) - Dtr <- array(c(tmp_p[1,1], tmp_p[2,1], 1- tmp_p[1,1], 0, tmp_p[3,1], 1 - tmp_p[3,1], 0,0,1), dim=c(S, S)) + Dtr <- array(c(tmp_p[1,1], tmp_p[2,1], 1- tmp_p[1,1] - tmp_p[2,1], 0, tmp_p[3,1], 1 - tmp_p[3,1], 0,0,1), dim=c(S, S)) latent_states[1,] <- Dtr %*% init_state_vec for (t in 2:T){ latent_states[t,] <- (Dtr %*% Mnt) %*% latent_states[t-1,] @@ -82,6 +83,6 @@ custom_nprior_generator <- function(true_prival){ ) } datasets <- custom_nprior_generator(true_prival) -backend <- cmdstan_sample_SBC_backend(DMSep, iter_warmup = 200, iter_sampling = 200) -results <- compute_results(datasets, backend) -plot_ecdf_diff(results) +backend <- cmdstan_sample_SBC_backend(DMSep, iter_warmup = 500, iter_sampling = 200) +results_new <- compute_results(datasets, backend) +plot_ecdf_diff(results_new) diff --git a/data/DMSep_validation_trueparam.csv b/data/DMSep_validation_trueparam.csv index a1583d5..35f65a1 100644 --- a/data/DMSep_validation_trueparam.csv +++ b/data/DMSep_validation_trueparam.csv @@ -1,340 +1,127 @@ -"","x" -"rate[1,1]",0.709953676374546 -"rate[2,1]",0.656246558252476 -"rate[3,1]",0.726240825741063 -"rate[4,1]",1.21616356277223e+307 -"rate[1,2]",0.227760884481376 -"rate[2,2]",0.317460219408028 -"rate[3,2]",0.245409653484201 -"rate[4,2]",9.26565583862199e+295 -"rate[1,3]",0.848362900141154 -"rate[2,3]",0.563671128800179 -"rate[3,3]",0.764742680547244 -"rate[4,3]",0.645955577876658 -"p",0.786249851206712 -"q",0.821510571848719 -"DM_pow[1,1,1]",0.392857721324671 -"DM_pow[2,1,1]",0.346233666312045 -"DM_pow[3,1,1]",0.341878311883544 -"DM_pow[4,1,1]",0.341226581999622 -"DM_pow[5,1,1]",0.341106106073424 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