inst/figure/figure_code/toy_eg.R
In eifer4/CoarsePosteriorSummary: Interpretable Model Summaries Using the Wasserstein Distance
rm(list=ls())
set.seed(236161905)
require(SLIMpaper)
figure.path <- file.path("inst","figure","toy_eg")
corrs <- c(0,0.5,0.9)
pres <- FALSE # is this for presentation
interactive <- FALSE # is this an interactive run
#### function to get ranks and plot ####
rankplot <- function(mod, color, label, base_size,
xlab = NULL, ylab = NULL) {
which.sel.orig <- sapply(mod$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# nzero <- mod$nzero[-which(colSums(which.sel.orig)==0)]
rmv <- -which(colSums(which.sel.orig)==0)
if(length(rmv) == 0) {
nzero <-mod$nzero
} else {
nzero <-mod$nzero[rmv]
which.sel.orig <- which.sel.orig[,rmv]
}
# which.sel.orig <- which.sel.orig[,-which(colSums(which.sel.orig)==0)]
which.sel <- matrix(0, nrow=p, ncol=p)
for(i in seq_along(nzero)){
which.sel[,nzero[i]] <- which.sel.orig[,i]
}
included.df_singcompare <- data.frame(Included = c(t(which.sel)),
Variable = factor(rep(c(1:p),each=p)),
Active = factor(rep(1:p, p)))
if(is.null(xlab)) xlab <- "Variable Number"
if(is.null(ylab)) ylab <- "Number Active Coefficients"
p <- ggplot2::ggplot(included.df_singcompare, ggplot2::aes(y=Active, x=Variable)) +
ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "steelblue") +
ggplot2::scale_fill_gradient(low = "white", high = color) +
ggplot2::theme_bw(base_size) + ggplot2::ylab(ylab) +
ggplot2::xlab(xlab) +
ggplot2::theme(legend.position="none") +
# ggplot2::ggtitle(bquote(L[1] ~ "Selection"))
ggplot2::ggtitle(label)
return(p)
}
#### Setup original data for posterior and for interpretable ####
target <- get_normal_linear_model()
n.samp <- 1000
n <- 1024
p_star <- 6
base_size <- if(pres) {
20
} else {
11
}
param <- target$rparam()
param$theta <- param$theta[1:p_star]
param$theta <-c(2, -0.1, -0.2, 1.3, 1.4, 1.5)
X_single <- cbind(1,100,90,0.01,0.01,0.01)
cl <- parallel::makeCluster(parallel::detectCores()-1)
doParallel::registerDoParallel(cl)
for( corr in corrs){
corr_fn <- gsub("[.]", "_",corr)
cat("Correlation:\n")
print(corr)
target$X$corr <- corr
X <- target$X$rX(n, target$X$corr, p_star)
# newCorr <- diag(p-1)
# newCorr[1:2,1:2] <- newCorr[3:4,3:4] <- newCorr[5:6,5:6] <- 0.5
# diag(newCorr) <- 1
# # X[,-1] <- X[,-1] %*% chol(newCorr)
data <- target$rdata(n, X, c(param$theta),
param$sigma2, corr = 0, scale = FALSE)
single_data <- target$rdata(1, X_single, c(param$theta),
param$sigma2, corr = 0, scale = FALSE)
Y <- data$Y - 2
single_Y <- single_data$Y - 2
X <- X[,-1]
X_sing <- X_single[,-1,drop=FALSE]
p <- p_star-1
hyperparameters <- list(mu = rep(0,p), sigma=diag(p), alpha = 10, beta = 10)
post <- target$rpost(n.samp, X, Y, hyperparameters,
method = "conjugate", X.test = X_sing)
cat("Variable Importance Order\n")
print(WPVI(X=X_sing, Y=post$test$eta, theta=post$theta, mean.fun = NULL,
p = 2, ground_p = 2, transport.method = "exact",
parallel = cl))
suffstat <- sufficientStatistics(X_sing, post$test$eta, post$theta, list(same = TRUE,
method = "selection.variable",
transport.method = "exact",
epsilon = 0.05,
niter = 100))
selection <- W2L1(X_sing, NULL, post$theta, family="gaussian",
method = "selection.variable",
penalty="mcp.net", nlambda = 1e2, alpha = .99,
infimum.maxit = 1e2, gamma = 1.5,
maxit = 1e4,
transport.method = "hilbert", penalty.factor = 1/abs(suffstat$XtY),
lambda.min.ratio = 1e-10, display.progress = TRUE)#,
plot.limep_L1 <- rankplot(mod = selection, color = "steelblue",
label = bquote(L[1]),
base_size = base_size,
xlab = "", ylab = "")
if(interactive) print(plot.limep_L1)
# which.sel.orig <- sapply(selection$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# nzero <- selection$nzero[-which(colSums(which.sel.orig)==0)]
# which.sel.orig <- which.sel.orig[,-which(colSums(which.sel.orig)==0)]
# which.sel <- matrix(0, nrow=p, ncol=p)
# for(i in seq_along(nzero)){
# which.sel[,nzero[i]] <- which.sel.orig[,i]
# }
#
# # included.df_sing <- data.frame(Included = c(t(which.sel.orig)),
# # Variable = factor(rep(c(1:p),each=length(nzero))),
# # Active = factor(rep(nzero, p)))
# # ggplot2::ggplot(included.df_sing, ggplot2::aes(y=Active, x=Variable)) +
# # ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# # ggplot2::scale_fill_gradient(low = "white", high = "steelblue") +
# # ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# # ggplot2::theme(legend.position="none")
#
# included.df_singcompare <- data.frame(Included = c(t(which.sel)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
# plot.limep_L1 <- ggplot2::ggplot(included.df_singcompare, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "steelblue") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle(bquote(L[1] ~ "Selection"))
#### IP (LIME-p) ####
# cl <- parallel::makeCluster(parallel::detectCores()-1)
# doParallel::registerDoParallel(cl)
BP <- W2IP(X_sing, post$test$eta, post$theta, transport.method = "univariate.approximation.pwr",
solution.method = "cone", display.progress = TRUE,
parallel = cl)
# doParallel::stopImplicitCluster()
# parallel::stopCluster(cl)
plot.limep_BP <- rankplot(mod = BP, color = "forestgreen",
label = "Binary Program",
base_size = base_size,
xlab = "", ylab = "")
if(interactive) print(plot.limep_BP)
# which.sel.orig.ip <- sapply(IP$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# rmv <- -which(colSums(which.sel.orig.ip)==0)
# if(length(rmv) == 0) {
# nzero.ip <-IP$nzero
# } else {
# nzero.ip <-IP$nzero[rmv]
# which.sel.orig.ip <- which.sel.orig.ip[,rmv]
# }
#
# which.sel.ip <- matrix(0, nrow=p, ncol=p)
# for(i in seq_along(nzero.ip)){
# which.sel.ip[,nzero.ip[i]] <- which.sel.orig.ip[,i]
# }
#
# ip.df_sing <- data.frame(Included = c(t(which.sel.ip)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
#
# plot.limep_IP <-ggplot2::ggplot(ip.df_sing, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "forestgreen") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle("I.P. Selection")
#### L0 (LIME-p) ####
# cl <- parallel::makeCluster(parallel::detectCores()-1)
# doParallel::registerDoParallel(cl)
l0ideal <- WPL0(X_sing, NULL, post$theta,p=2,ground_p = 2,
transport.method = "hilbert",
method = "selection.variable",
parallel = cl)
# doParallel::stopImplicitCluster()
# parallel::stopCluster(cl)
plot.limep_L0 <- rankplot(mod = l0ideal, color = "firebrick3",
label = bquote(L[0]),
base_size = base_size)
if(interactive) print(plot.limep_L0)
# which.ideal.l0 <- matrix(0, nrow=p, ncol=p)
# for (i in 1:p) {
# which.ideal.l0[l0ideal$minCombPerActive[[i]],i] <- 1
# }
#
# ideal.df_sing <- data.frame(Included = c(t(which.ideal.l0)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
#
# plot.limep_L0 <-ggplot2::ggplot(ideal.df_sing, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "firebrick3") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle(bquote(L[0] ~ "Selection"))
filename <- paste0("selection_order_limep_",corr_fn,".pdf")
pdf(file.path(figure.path, filename),
width = 7.5, height = 3)
gridExtra::grid.arrange(plot.limep_L0,
plot.limep_L1, plot.limep_BP, nrow=1)
dev.off()
#### Ridge Plots (LIME-p) ####
# rr <- ridgePlot(list("L0" = l0ideal,
# "L1" = selection,
# "B.P." = BP),
# minCoef=1, maxCoef=6, full = c(post$test$eta), xlab="")
# rr <- rr + ggridges::theme_ridges(base_size) + ggplot2::scale_fill_manual(
# breaks = c("L0","L1", "B.P."),
# labels = c(bquote(L[0]),bquote(L[1]), "B.P."),
# values = c("forestgreen", "firebrick3", "steelblue","red")) +
# ggplot2::ggtitle("LIME-p") + ggplot2::ylab("")
# # rr$data$Method <- factor(rr$data$Method, levels = c("L0","L1"), labels = c(expression(L[0]), expression(L[1])))
# filename <- paste0("ridge_plot_limep_",corr,".pdf")
# pdf(file.path(figure.path, filename),
# width = 4, height = 3)
# print(rr)
# dev.off()
#### LIME-a estimation ####
Sigma <- cov(X)
n_neighborhood <- 100
X_neighborhood <- SLIMpaper::rmvnorm(n_neighborhood,
mean = X_sing,
covariance = Sigma/n)
proj <-W2L1(X_neighborhood, NULL, post$theta, family="gaussian",
method = "projection",
penalty="mcp.net", nlambda = 1e2, alpha = .99,
infimum.maxit = 1, gamma = 1.01,
maxit = 1e4, lambda.min.ratio = 1e-10, display.progress = TRUE)
plot.limea_L1 <- rankplot(mod = proj, color = "steelblue",
label = bquote(L[1] ~ "Selection"),
base_size = base_size)
if(interactive) print(plot.limea_L1)
# which.sel.orig.proj <- sapply(proj$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# rmv <- -which(colSums(which.sel.orig.proj)==0)
# if(length(rmv) == 0) {
# nzero.proj <-proj$nzero
# } else {
# nzero.proj <-proj$nzero[rmv]
# which.sel.orig.proj <- which.sel.orig.proj[,rmv]
# }
#
# which.sel.proj <- matrix(0, nrow=p, ncol=p)
# for(i in seq_along(nzero.proj)){
# which.sel.proj[,nzero.proj[i]] <- which.sel.orig.proj[,i]
# }
#
# included.df_proj <- data.frame(Included = c(t(which.sel.proj)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
# plot.limea_L1 <- ggplot2::ggplot(included.df_proj, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "steelblue") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle(bquote(L[1] ~ "Selection"))
#
#
# cl <- parallel::makeCluster(parallel::detectCores()-1)
# doParallel::registerDoParallel(cl)
l0_limea <- WPL0(X_neighborhood, NULL, post$theta,p=2, ground_p = 2,
transport.method = "hilbert",
method = "projection",
parallel = cl)
# doParallel::stopImplicitCluster()
plot.limea_l0 <- rankplot(mod = l0_limea, color = "firebrick3",
label = bquote(L[0] ~ "Selection"),
base_size = base_size)
if(interactive) print(plot.limea_l0)
#
# which.sel.orig.l0_limea <- sapply(l0_limea$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# rmv <- -which(colSums(which.sel.orig.l0_limea)==0)
# if(length(rmv) == 0) {
# nzero.l0_limea <-l0_limea$nzero
# } else {
# nzero.l0_limea <-l0_limea$nzero[rmv]
# which.sel.orig.l0_limea <- which.sel.orig.l0_limea[,rmv]
# }
#
# which.sel.l0_limea <- matrix(0, nrow=p, ncol=p)
# for(i in seq_along(nzero.l0_limea)){
# which.sel.l0_limea[,nzero.l0_limea[i]] <- which.sel.orig.l0_limea[,i]
# }
#
# included.df_l0_limea <- data.frame(Included = c(t(which.sel.l0_limea)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
# plot.limea_l0 <- ggplot2::ggplot(included.df_l0_limea, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "firebrick3") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle(bquote(L[0] ~ "Selection"))
filename <- paste0("selection_order_limea_",corr_fn,".pdf")
pdf(file.path(figure.path, filename),
width = 7.5, height = 3)
gridExtra::grid.arrange(plot.limep_L0,
plot.limep_L1, nrow=1, ncol=3)
dev.off()
#### Ridge Plots (LIME-a) ####
# rr2 <- ridgePlot(list("L0" = l0_limea,
# "L1" = proj),
# minCoef=1, maxCoef=6, full = c(post$test$eta),
# xlab="Posterior Predictive Mean",
# scale = 4)
# rr2 <- rr2 + ggridges::theme_ridges(base_size) +
# ggplot2::scale_fill_manual(
# breaks = c("L0","L1"),
# labels = c(bquote(L[0]),bquote(L[1])),
# values = c( "firebrick3", "steelblue","red")) +
# ggplot2::ggtitle("LIME-a") +
# ggplot2::theme(legend.position = "none")
# # rr$data$Method <- factor(rr$data$Method, levels = c("L0","L1"), labels = c(expression(L[0]), expression(L[1])))
# filename <- paste0("ridge_plot_limea_",corr,".pdf")
# pdf(file.path(figure.path, filename),
# width = 3.25, height = 3)
# print(rr2)
# dev.off()
scale <- if(corr == 0.5) {
1.3
} else {
4
}
rr <- ridgePlot(list("LIME-a" = list("L0" = l0_limea,
"L1" = proj),
"LIME-p" = list("L0" = l0ideal,
"L1" = selection,
"B.P." = BP)),
minCoef=1, maxCoef=5, full = c(post$test$eta),
xlab="Posterior Predictive Mean",
scale = scale)
rr <- rr + ggridges::theme_ridges(base_size) + ggplot2::scale_fill_manual(
breaks = c("L0","L1", "B.P."),
labels = c(bquote(L[0]),bquote(L[1]), "B.P."),
values = c("firebrick3", "steelblue","forestgreen","red"))
# rr$data$Method <- factor(rr$data$Method, levels = c("L0","L1"), labels = c(expression(L[0]), expression(L[1])))
filename <- paste0("ridge_plot_",corr_fn,".pdf")
pdf(file.path(figure.path, filename),
width = 7.5, height = 3.5)
print(rr)
dev.off()
}
doParallel::stopImplicitCluster()
parallel::stopCluster(cl)
eifer4/CoarsePosteriorSummary documentation built on April 10, 2021, 12:40 p.m.
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rm(list=ls())
set.seed(236161905)
require(SLIMpaper)
figure.path <- file.path("inst","figure","toy_eg")
corrs <- c(0,0.5,0.9)
pres <- FALSE # is this for presentation
interactive <- FALSE # is this an interactive run
#### function to get ranks and plot ####
rankplot <- function(mod, color, label, base_size,
xlab = NULL, ylab = NULL) {
which.sel.orig <- sapply(mod$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# nzero <- mod$nzero[-which(colSums(which.sel.orig)==0)]
rmv <- -which(colSums(which.sel.orig)==0)
if(length(rmv) == 0) {
nzero <-mod$nzero
} else {
nzero <-mod$nzero[rmv]
which.sel.orig <- which.sel.orig[,rmv]
}
# which.sel.orig <- which.sel.orig[,-which(colSums(which.sel.orig)==0)]
which.sel <- matrix(0, nrow=p, ncol=p)
for(i in seq_along(nzero)){
which.sel[,nzero[i]] <- which.sel.orig[,i]
}
included.df_singcompare <- data.frame(Included = c(t(which.sel)),
Variable = factor(rep(c(1:p),each=p)),
Active = factor(rep(1:p, p)))
if(is.null(xlab)) xlab <- "Variable Number"
if(is.null(ylab)) ylab <- "Number Active Coefficients"
p <- ggplot2::ggplot(included.df_singcompare, ggplot2::aes(y=Active, x=Variable)) +
ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "steelblue") +
ggplot2::scale_fill_gradient(low = "white", high = color) +
ggplot2::theme_bw(base_size) + ggplot2::ylab(ylab) +
ggplot2::xlab(xlab) +
ggplot2::theme(legend.position="none") +
# ggplot2::ggtitle(bquote(L[1] ~ "Selection"))
ggplot2::ggtitle(label)
return(p)
}
#### Setup original data for posterior and for interpretable ####
target <- get_normal_linear_model()
n.samp <- 1000
n <- 1024
p_star <- 6
base_size <- if(pres) {
20
} else {
11
}
param <- target$rparam()
param$theta <- param$theta[1:p_star]
param$theta <-c(2, -0.1, -0.2, 1.3, 1.4, 1.5)
X_single <- cbind(1,100,90,0.01,0.01,0.01)
cl <- parallel::makeCluster(parallel::detectCores()-1)
doParallel::registerDoParallel(cl)
for( corr in corrs){
corr_fn <- gsub("[.]", "_",corr)
cat("Correlation:\n")
print(corr)
target$X$corr <- corr
X <- target$X$rX(n, target$X$corr, p_star)
# newCorr <- diag(p-1)
# newCorr[1:2,1:2] <- newCorr[3:4,3:4] <- newCorr[5:6,5:6] <- 0.5
# diag(newCorr) <- 1
# # X[,-1] <- X[,-1] %*% chol(newCorr)
data <- target$rdata(n, X, c(param$theta),
param$sigma2, corr = 0, scale = FALSE)
single_data <- target$rdata(1, X_single, c(param$theta),
param$sigma2, corr = 0, scale = FALSE)
Y <- data$Y - 2
single_Y <- single_data$Y - 2
X <- X[,-1]
X_sing <- X_single[,-1,drop=FALSE]
p <- p_star-1
hyperparameters <- list(mu = rep(0,p), sigma=diag(p), alpha = 10, beta = 10)
post <- target$rpost(n.samp, X, Y, hyperparameters,
method = "conjugate", X.test = X_sing)
cat("Variable Importance Order\n")
print(WPVI(X=X_sing, Y=post$test$eta, theta=post$theta, mean.fun = NULL,
p = 2, ground_p = 2, transport.method = "exact",
parallel = cl))
suffstat <- sufficientStatistics(X_sing, post$test$eta, post$theta, list(same = TRUE,
method = "selection.variable",
transport.method = "exact",
epsilon = 0.05,
niter = 100))
selection <- W2L1(X_sing, NULL, post$theta, family="gaussian",
method = "selection.variable",
penalty="mcp.net", nlambda = 1e2, alpha = .99,
infimum.maxit = 1e2, gamma = 1.5,
maxit = 1e4,
transport.method = "hilbert", penalty.factor = 1/abs(suffstat$XtY),
lambda.min.ratio = 1e-10, display.progress = TRUE)#,
plot.limep_L1 <- rankplot(mod = selection, color = "steelblue",
label = bquote(L[1]),
base_size = base_size,
xlab = "", ylab = "")
if(interactive) print(plot.limep_L1)
# which.sel.orig <- sapply(selection$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# nzero <- selection$nzero[-which(colSums(which.sel.orig)==0)]
# which.sel.orig <- which.sel.orig[,-which(colSums(which.sel.orig)==0)]
# which.sel <- matrix(0, nrow=p, ncol=p)
# for(i in seq_along(nzero)){
# which.sel[,nzero[i]] <- which.sel.orig[,i]
# }
#
# # included.df_sing <- data.frame(Included = c(t(which.sel.orig)),
# # Variable = factor(rep(c(1:p),each=length(nzero))),
# # Active = factor(rep(nzero, p)))
# # ggplot2::ggplot(included.df_sing, ggplot2::aes(y=Active, x=Variable)) +
# # ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# # ggplot2::scale_fill_gradient(low = "white", high = "steelblue") +
# # ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# # ggplot2::theme(legend.position="none")
#
# included.df_singcompare <- data.frame(Included = c(t(which.sel)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
# plot.limep_L1 <- ggplot2::ggplot(included.df_singcompare, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "steelblue") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle(bquote(L[1] ~ "Selection"))
#### IP (LIME-p) ####
# cl <- parallel::makeCluster(parallel::detectCores()-1)
# doParallel::registerDoParallel(cl)
BP <- W2IP(X_sing, post$test$eta, post$theta, transport.method = "univariate.approximation.pwr",
solution.method = "cone", display.progress = TRUE,
parallel = cl)
# doParallel::stopImplicitCluster()
# parallel::stopCluster(cl)
plot.limep_BP <- rankplot(mod = BP, color = "forestgreen",
label = "Binary Program",
base_size = base_size,
xlab = "", ylab = "")
if(interactive) print(plot.limep_BP)
# which.sel.orig.ip <- sapply(IP$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# rmv <- -which(colSums(which.sel.orig.ip)==0)
# if(length(rmv) == 0) {
# nzero.ip <-IP$nzero
# } else {
# nzero.ip <-IP$nzero[rmv]
# which.sel.orig.ip <- which.sel.orig.ip[,rmv]
# }
#
# which.sel.ip <- matrix(0, nrow=p, ncol=p)
# for(i in seq_along(nzero.ip)){
# which.sel.ip[,nzero.ip[i]] <- which.sel.orig.ip[,i]
# }
#
# ip.df_sing <- data.frame(Included = c(t(which.sel.ip)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
#
# plot.limep_IP <-ggplot2::ggplot(ip.df_sing, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "forestgreen") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle("I.P. Selection")
#### L0 (LIME-p) ####
# cl <- parallel::makeCluster(parallel::detectCores()-1)
# doParallel::registerDoParallel(cl)
l0ideal <- WPL0(X_sing, NULL, post$theta,p=2,ground_p = 2,
transport.method = "hilbert",
method = "selection.variable",
parallel = cl)
# doParallel::stopImplicitCluster()
# parallel::stopCluster(cl)
plot.limep_L0 <- rankplot(mod = l0ideal, color = "firebrick3",
label = bquote(L[0]),
base_size = base_size)
if(interactive) print(plot.limep_L0)
# which.ideal.l0 <- matrix(0, nrow=p, ncol=p)
# for (i in 1:p) {
# which.ideal.l0[l0ideal$minCombPerActive[[i]],i] <- 1
# }
#
# ideal.df_sing <- data.frame(Included = c(t(which.ideal.l0)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
#
# plot.limep_L0 <-ggplot2::ggplot(ideal.df_sing, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "firebrick3") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle(bquote(L[0] ~ "Selection"))
filename <- paste0("selection_order_limep_",corr_fn,".pdf")
pdf(file.path(figure.path, filename),
width = 7.5, height = 3)
gridExtra::grid.arrange(plot.limep_L0,
plot.limep_L1, plot.limep_BP, nrow=1)
dev.off()
#### Ridge Plots (LIME-p) ####
# rr <- ridgePlot(list("L0" = l0ideal,
# "L1" = selection,
# "B.P." = BP),
# minCoef=1, maxCoef=6, full = c(post$test$eta), xlab="")
# rr <- rr + ggridges::theme_ridges(base_size) + ggplot2::scale_fill_manual(
# breaks = c("L0","L1", "B.P."),
# labels = c(bquote(L[0]),bquote(L[1]), "B.P."),
# values = c("forestgreen", "firebrick3", "steelblue","red")) +
# ggplot2::ggtitle("LIME-p") + ggplot2::ylab("")
# # rr$data$Method <- factor(rr$data$Method, levels = c("L0","L1"), labels = c(expression(L[0]), expression(L[1])))
# filename <- paste0("ridge_plot_limep_",corr,".pdf")
# pdf(file.path(figure.path, filename),
# width = 4, height = 3)
# print(rr)
# dev.off()
#### LIME-a estimation ####
Sigma <- cov(X)
n_neighborhood <- 100
X_neighborhood <- SLIMpaper::rmvnorm(n_neighborhood,
mean = X_sing,
covariance = Sigma/n)
proj <-W2L1(X_neighborhood, NULL, post$theta, family="gaussian",
method = "projection",
penalty="mcp.net", nlambda = 1e2, alpha = .99,
infimum.maxit = 1, gamma = 1.01,
maxit = 1e4, lambda.min.ratio = 1e-10, display.progress = TRUE)
plot.limea_L1 <- rankplot(mod = proj, color = "steelblue",
label = bquote(L[1] ~ "Selection"),
base_size = base_size)
if(interactive) print(plot.limea_L1)
# which.sel.orig.proj <- sapply(proj$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# rmv <- -which(colSums(which.sel.orig.proj)==0)
# if(length(rmv) == 0) {
# nzero.proj <-proj$nzero
# } else {
# nzero.proj <-proj$nzero[rmv]
# which.sel.orig.proj <- which.sel.orig.proj[,rmv]
# }
#
# which.sel.proj <- matrix(0, nrow=p, ncol=p)
# for(i in seq_along(nzero.proj)){
# which.sel.proj[,nzero.proj[i]] <- which.sel.orig.proj[,i]
# }
#
# included.df_proj <- data.frame(Included = c(t(which.sel.proj)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
# plot.limea_L1 <- ggplot2::ggplot(included.df_proj, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "steelblue") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle(bquote(L[1] ~ "Selection"))
#
#
# cl <- parallel::makeCluster(parallel::detectCores()-1)
# doParallel::registerDoParallel(cl)
l0_limea <- WPL0(X_neighborhood, NULL, post$theta,p=2, ground_p = 2,
transport.method = "hilbert",
method = "projection",
parallel = cl)
# doParallel::stopImplicitCluster()
plot.limea_l0 <- rankplot(mod = l0_limea, color = "firebrick3",
label = bquote(L[0] ~ "Selection"),
base_size = base_size)
if(interactive) print(plot.limea_l0)
#
# which.sel.orig.l0_limea <- sapply(l0_limea$theta, function(tt) as.numeric(rowSums(abs(tt)) > 0))
# rmv <- -which(colSums(which.sel.orig.l0_limea)==0)
# if(length(rmv) == 0) {
# nzero.l0_limea <-l0_limea$nzero
# } else {
# nzero.l0_limea <-l0_limea$nzero[rmv]
# which.sel.orig.l0_limea <- which.sel.orig.l0_limea[,rmv]
# }
#
# which.sel.l0_limea <- matrix(0, nrow=p, ncol=p)
# for(i in seq_along(nzero.l0_limea)){
# which.sel.l0_limea[,nzero.l0_limea[i]] <- which.sel.orig.l0_limea[,i]
# }
#
# included.df_l0_limea <- data.frame(Included = c(t(which.sel.l0_limea)),
# Variable = factor(rep(c(1:p),each=p)),
# Active = factor(rep(1:p, p)))
# plot.limea_l0 <- ggplot2::ggplot(included.df_l0_limea, ggplot2::aes(y=Active, x=Variable)) +
# ggplot2::geom_tile(ggplot2::aes(fill = Included), colour = "white") +
# ggplot2::scale_fill_gradient(low = "white", high = "firebrick3") +
# ggplot2::theme_bw(base_size) + ggplot2::ylab("Number Active Coefficients") + ggplot2::xlab("Variable Number") +
# ggplot2::theme(legend.position="none") + ggplot2::ggtitle(bquote(L[0] ~ "Selection"))
filename <- paste0("selection_order_limea_",corr_fn,".pdf")
pdf(file.path(figure.path, filename),
width = 7.5, height = 3)
gridExtra::grid.arrange(plot.limep_L0,
plot.limep_L1, nrow=1, ncol=3)
dev.off()
#### Ridge Plots (LIME-a) ####
# rr2 <- ridgePlot(list("L0" = l0_limea,
# "L1" = proj),
# minCoef=1, maxCoef=6, full = c(post$test$eta),
# xlab="Posterior Predictive Mean",
# scale = 4)
# rr2 <- rr2 + ggridges::theme_ridges(base_size) +
# ggplot2::scale_fill_manual(
# breaks = c("L0","L1"),
# labels = c(bquote(L[0]),bquote(L[1])),
# values = c( "firebrick3", "steelblue","red")) +
# ggplot2::ggtitle("LIME-a") +
# ggplot2::theme(legend.position = "none")
# # rr$data$Method <- factor(rr$data$Method, levels = c("L0","L1"), labels = c(expression(L[0]), expression(L[1])))
# filename <- paste0("ridge_plot_limea_",corr,".pdf")
# pdf(file.path(figure.path, filename),
# width = 3.25, height = 3)
# print(rr2)
# dev.off()
scale <- if(corr == 0.5) {
1.3
} else {
4
}
rr <- ridgePlot(list("LIME-a" = list("L0" = l0_limea,
"L1" = proj),
"LIME-p" = list("L0" = l0ideal,
"L1" = selection,
"B.P." = BP)),
minCoef=1, maxCoef=5, full = c(post$test$eta),
xlab="Posterior Predictive Mean",
scale = scale)
rr <- rr + ggridges::theme_ridges(base_size) + ggplot2::scale_fill_manual(
breaks = c("L0","L1", "B.P."),
labels = c(bquote(L[0]),bquote(L[1]), "B.P."),
values = c("firebrick3", "steelblue","forestgreen","red"))
# rr$data$Method <- factor(rr$data$Method, levels = c("L0","L1"), labels = c(expression(L[0]), expression(L[1])))
filename <- paste0("ridge_plot_",corr_fn,".pdf")
pdf(file.path(figure.path, filename),
width = 7.5, height = 3.5)
print(rr)
dev.off()
}
doParallel::stopImplicitCluster()
parallel::stopCluster(cl)
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