Fixed typo in commit message

Author: AdrienBibal

.Rhistory

@@ -0,0 +1,59 @@
+#######################################
+#### Main function for BIR         ####
+#######################################
+rm(list = ls())
+library(glmnet) # Lasso
+library(pbapply)
+library(GenSA) # simulated annealing
+#### File paths ####
+data.path <- "Datasets/"
+out.path <- "Results/"
+function.path <- "Functions/"
+source.files <- list.files(path = function.path, recursive = TRUE)
+invisible(sapply(source.files, function(x) source(file = paste0(function.path, x))))
+Fe <- read.csv(paste0(data.path, "dataset.csv"))
+X <- read.csv(paste0(data.path, "embedding.csv"), header=F)
+lambda.vals <- seq(0.0001, 3.5, length = 10)
+# lambda.vals <- exp(seq(log(0.0001), log(3.5), length.out = 20))
+#### Run BIR ####
+# Prepare fold ids
+seed <- 155000
+set.seed(seed)
+eval.res.lambda <- c()
+lambda.vals.index = 0
+best_lambda.norm = 0.06
+# Compute BIR on the full dataset with the best lambda
+res <- RunBIR(X = scale(X, center=T, scale=F),
+Fe = scale(Fe, center=T, scale=T),
+lambda = best_lambda.norm)
+# Compute BIR on the full dataset with the best lambda
+res <- RunBIR(X = scale(X, center=T, scale=F),
+Fe = Fe,
+lambda = best_lambda.norm)
+View(Fe)
+# Compute BIR on the full dataset with the best lambda
+res <- RunBIR(X = scale(X, center=T, scale=F),
+Fe = scale(Fe, center=T, scale=F),
+lambda = best_lambda.norm)
+# Compute BIR on the full dataset with the best lambda
+res <- RunBIR(X = scale(X, center=T, scale=F),
+Fe = scale(Fe, center=T, scale=T),
+lambda = best_lambda.norm)
+Fe
+# Some elements of Fe can have a standard deviation (sd) equal to 0, which is an issue when scaling.
+# In ordre to solve the problem, the sd for these columns is set to 1.
+Fe.sd <- apply(Fe, 2, sd) # Check which columns have sd = 0
+zero.sd <- which(Fe.sd == 0)
+if (length(wh.zero.sd) > 0){
+Fe.sd[wh.zero.sd] <- 1
+}
+if (length(zero.sd) > 0){
+Fe.sd[zero.sd] <- 1
+}
+# Compute BIR on the full dataset with the best lambda
+res <- RunBIR(X = scale(X, center=T, scale=F),
+Fe = scale(Fe, center=T, scale=Fe.sd),
+lambda = best_lambda.norm)
+res
+save(res, file = paste0(out.path, "result.RData"))
+save(res, file = paste0(out.path, "result_v2.RData"), version = 2)

BIR.R

@@ -75,9 +75,17 @@ eval.res.lambda <- do.call(rbind.data.frame, eval.res.lambda)
 best_lambda.index <- which.min(eval.res.lambda$avg_MSE)
 best_lambda.norm <- eval.res.lambda$lambda.norm[best_lambda.index]
 
+# Some elements of Fe can have a standard deviation (sd) equal to 0, which is an issue when scaling.
+# In ordre to solve the problem, the sd for these columns is set to 1.
+Fe.sd <- apply(Fe, 2, sd) # Check which columns have sd = 0
+zero.sd <- which(Fe.sd == 0)
+if (length(zero.sd) > 0){
+  Fe.sd[zero.sd] <- 1
+}
+
 # Compute BIR on the full dataset with the best lambda
 res <- RunBIR(X = scale(X, center=T, scale=F), 
-              Fe = scale(Fe, center=T, scale=T),
+              Fe = scale(Fe, center=T, scale=Fe.sd),
               lambda = best_lambda.norm)
 
 save(res, file = paste0(out.path, "result.RData"))