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### R code from vignette source 'pauwels2014.Rnw'
### Encoding: ASCII
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### code chunk number 1: preliminaries
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#options(prompt = " ")
#options(continue = " ")
options(width=70)
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### code chunk number 2: figPlot
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library(pauwels2014)
data(knobjs)
sapply(
1:length(knobjs),
function(k){
assign(names(knobjs)[k], knobjs[[k]], envir = .GlobalEnv)
}
)
data(exps)
mean_risks_act_mult <- read_knobjs( sprintf("knobjActMult%s", 1:10) )
mean_risks_dream6_mult <- read_knobjs( sprintf("knobjDream6Mult%s", 1:10) )
mean_risks_rand_mult <- read_knobjs( sprintf("knobjRandMult%s", 1:10) )
mean_risks_act_mult <- compute_mean_risks(mean_risks_act_mult, "Bayesian active")
mean_risks_dream6_mult <- compute_mean_risks(mean_risks_dream6_mult, "Dream6")
mean_risks_rand_mult <- compute_mean_risks(mean_risks_rand_mult, "Random")
data_to_plot <- rbind(mean_risks_act_mult,mean_risks_dream6_mult, mean_risks_rand_mult)
ggplot(data = data_to_plot, aes(x=cost, y=risk)) +
theme_bw() +
facet_grid(.~type) +
scale_y_log10() +
geom_line(aes(group = chain), alpha = 0.2) +
geom_point() +
stat_smooth(method = "loess", colour = "black") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
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### code chunk number 3: fig1
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library(pauwels2014)
data(knobjs)
sapply(
1:length(knobjs),
function(k){
assign(names(knobjs)[k], knobjs[[k]], envir = .GlobalEnv)
}
)
data(exps)
mean_risks_act_mult <- read_knobjs( sprintf("knobjActMult%s", 1:10) )
mean_risks_dream6_mult <- read_knobjs( sprintf("knobjDream6Mult%s", 1:10) )
mean_risks_rand_mult <- read_knobjs( sprintf("knobjRandMult%s", 1:10) )
mean_risks_act_mult <- compute_mean_risks(mean_risks_act_mult, "Bayesian active")
mean_risks_dream6_mult <- compute_mean_risks(mean_risks_dream6_mult, "Dream6")
mean_risks_rand_mult <- compute_mean_risks(mean_risks_rand_mult, "Random")
data_to_plot <- rbind(mean_risks_act_mult,mean_risks_dream6_mult, mean_risks_rand_mult)
ggplot(data = data_to_plot, aes(x=cost, y=risk)) +
theme_bw() +
facet_grid(.~type) +
scale_y_log10() +
geom_line(aes(group = chain), alpha = 0.2) +
geom_point() +
stat_smooth(method = "loess", colour = "black") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
###################################################
### code chunk number 4: preliminaries2
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rm(list = ls())
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### code chunk number 5: re-run (eval = FALSE)
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## ## Load datasets
## data(exps)
## data(experiment_list1)
## data(observables)
##
## ## Initialize a knowledge list
## knobj <- generate_our_knowledge(transform_params)
## knobj$datas[[1]] <- list(
## manip = experiment_list1$nothing,
## data = add_noise(
## simulate_experiment(
## knobj$global_parameters$true_params_T, knobj,
## experiment_list1$nothing)[
## knobj$global_parameters$tspan %in% observables[["mrnaLow"]]$reso,
## observables[["mrnaLow"]]$obs
## ]
## )
## )
## knobj$experiments <- paste("nothing", "mrnaLow")
##
## ## Update the knowledge list
## knobjActMult1 <- active_design(knobj, sample_function_multi_mod_weight, seed = 1, credits = 5000)
###################################################
### code chunk number 6: perform_simu_one (eval = FALSE)
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## ## Simulates the active learning experimental design with
## ## A simplified network
## ## Arguments to be passed
## ## active: O or 1
## ## multimodal search: 0 or 1
## ## random seed: integer
## ## Results are saved during the process
##
## args <- commandArgs(trailingOnly=TRUE)
## if(length(args) != 3){
## stop("Three argument should be passed: bool active (0 or 1), bool multimod (0 or 1), int seed")
## }
##
##
## active <- as.integer(as.numeric(args[1]))
## multi_mod <- as.logical(as.numeric(args[2]))
## seed <- as.integer(args[3])
##
##
## ## Define the home directory
## home <- "some_working_directory"
##
##
## ## Where to save the results
## file_to_save <- paste(
## home,
## "results_subdirectory/",
## "knobj",
## c("Rand","Act", "Dream6")[active +1], c("Sing","Mult")[multi_mod +1],
## seed,
## sep=""
## )
##
##
## ## Load datasets and library
## set.seed(seed)
## library(pauwels2014)
## data(exps)
## data(experiment_list1)
## data(observables)
##
##
## ## Initialize a knowledge list
## knobj <- generate_our_knowledge(transform_params)
## knobj$datas[[1]] <- list(
## manip = experiment_list1$nothing,
## data = add_noise(
## simulate_experiment(
## knobj$global_parameters$true_params_T,
## knobj,
## experiment_list1$nothing)[
## knobj$global_parameters$tspan %in% observables[["mrnaLow"]]$reso,
## observables[["mrnaLow"]]$obs
## ]
## )
## )
## knobj$experiments <- paste("nothing", "mrnaLow")
##
##
## ## Chosse a sample function
## if(multi_mod ){
## sample_function <- sample_function_multi_mod_weight
## }else{
## sample_function <- sample_function_single_mod
## }
##
##
## ## Update the knowledge list with the chosen strategy
## if(active==1){
## knobj <- active_design(knobj, sample_function, seed, credits = 5000, file_to_save = file_to_save)
## }
## if(active==0){
## knobj <- random_design(knobj, sample_function, exps, seed, credits = 5000, file_to_save = file_to_save)
## }
## if(active==2){
## knobj <- dream6_design(knobj, sample_function, exps, seed, credits = 5000, file_to_save = file_to_save)
## }
##
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### code chunk number 7: commands
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home <- "some_home/"
path_to_R <- "some_path/R"
n_seed <- 10
seeds <- 1:n_seed
active <- c(rep(2,n_seed),rep(1,2*n_seed),rep(0,n_seed))
multi_mod <- c(rep(1,2*n_seed),rep(0,n_seed),rep(1,n_seed))
commands <-
sprintf(
"%s --vanilla --args '%s' '%s' '%s' < %s",
path_to_R,
active,
multi_mod,
seeds,
paste(home, "perform_simu_one.R",
sep = ""
)
)
print(commands)
###################################################
### code chunk number 8: log_prior
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log_prior
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### code chunk number 9: log_prior
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log_likelihood
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### code chunk number 10: risk_theta_fun
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risk_theta_fun
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### code chunk number 11: tr_params
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knobj <- generate_our_knowledge(transform_params)
knobj$global_parameters$true_params_T
transform_params(knobj$global_parameters$true_params_T)
###################################################
### code chunk number 12: obserevables (eval = FALSE)
###################################################
## superHighRes <- 0:200/2
## highRes <- 0:50 * 2
## lowRes <- 0:25 * 4
##
## observables <- list(
## list(name = "p6", obs = c("time", "p6"),reso = superHighRes, cost = 400),
## list(name = "p7", obs = c("time", "p7"),reso = superHighRes, cost = 400),
## list(name = "p8", obs = c("time", "p8"),reso = superHighRes, cost = 400),
## list(name = "mrnaHigh", obs = c("time", "v6_mrna", "v7_mrna", "v8_mrna"), reso = highRes, cost = 1000),
## list(name = "mrnaLow", obs = c("time", "v6_mrna", "v7_mrna", "v8_mrna"), reso = lowRes, cost = 500)
## )
##
## names(observables) <- sapply(observables, FUN = function(x){x$name})
###################################################
### code chunk number 13: experiments (eval = FALSE)
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## delete_gene6 <- function(theta, init){
## theta[names(theta) %in% c("pro6_strength","rbs6_strength")] <- 0
## init[names(init) == "p6"] <- 0
## res <- c()
## res$theta <- theta
## res$initial_conditions <- init
## res$cost <- 800
## res
## }
##
## delete_gene7 <- function(theta, init){
## theta[names(theta) %in% c("pro7_strength","rbs8_strength")] <- 0
## init[names(init) == "p7"] <- 0
## res <- c()
## res$theta <- theta
## res$initial_conditions <- init
## res$cost <- 800
## res
## }
##
## delete_gene8 <- function(theta, init){
## theta[names(theta) %in% c("pro9_strength","rbs7_strength")] <- 0
## init[names(init) == "p8"] <- 0
## res <- c()
## res$theta <- theta
## res$initial_conditions <- init
## res$cost <- 800
## res
## }
##
##
##
## knockdown_gene6 <- function(theta, init){
## theta[names(theta) %in% "mrna6_degradation_rate"] <- 10
## res <- c()
## res$theta <- theta
## res$initial_conditions <- init
## res$cost <- 350
## res
## }
##
## knockdown_gene7 <- function(theta, init){
## theta[names(theta) %in% "mrna7_degradation_rate"] <- 10
## res <- c()
## res$theta <- theta
## res$initial_conditions <- init
## res$cost <- 350
## res
## }
##
## knockdown_gene8 <- function(theta, init){
## theta[names(theta) %in% "mrna8_degradation_rate"] <- 10
## res <- c()
## res$theta <- theta
## res$initial_conditions <- init
## res$cost <- 350
## res
## }
##
##
##
## decrease_rbs_gene6 <- function(theta, init){
## theta[names(theta) %in% "rbs6_strength"] <- theta[names(theta) %in% "rbs6_strength"] / 10
## res <- c()
## res$theta <- theta
## res$initial_conditions <- init
## res$cost <- 450
## res
## }
##
## decrease_rbs_gene7 <- function(theta, init){
## theta[names(theta) %in% "rbs8_strength"] <- theta[names(theta) %in% "rbs8_strength"] / 10
## res <- c()
## res$theta <- theta
## res$initial_conditions <- init
## res$cost <- 450
## res
## }
##
## decrease_rbs_gene8 <- function(theta, init){
## theta[names(theta) %in% "rbs7_strength"] <- theta[names(theta) %in% "rbs7_strength"] / 10
## res <- c()
## res$theta <- theta
## res$initial_conditions <- init
## res$cost <- 450
## res
## }
##
## nothing <- function(theta, init){
## list(theta = theta, initial_conditions = init, cost = 0)
## }
##
## experiment_list1 <- c()
## experiment_list1$delete_gene6 <- delete_gene6
## experiment_list1$delete_gene7 <- delete_gene7
## experiment_list1$delete_gene8 <- delete_gene8
## experiment_list1$knockdown_gene6 <- knockdown_gene6
## experiment_list1$knockdown_gene7 <- knockdown_gene7
## experiment_list1$knockdown_gene8 <- knockdown_gene8
## experiment_list1$decrease_rbs_gene6 <- decrease_rbs_gene6
## experiment_list1$decrease_rbs_gene7 <- decrease_rbs_gene7
## experiment_list1$decrease_rbs_gene8 <- decrease_rbs_gene8
## experiment_list1$nothing <- nothing
##
## exps <- lapply(names(experiment_list1), function(name){
## t(sapply(observables, function(obs){
## cbind(obs$name, obs$cost + experiment_list1[[name]](0,0)$cost, name)
## }
## ))
## }
## )
##
## exps <- Reduce(exps, f = function(s,t){rbind(s,t)})
##
## exps <- data.frame(Measurement = exps[,1], Cost = as.numeric(exps[,2]), exp = exps[,3])
###################################################
### code chunk number 14: add_noise
###################################################
add_noise
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