Some useful information of OCV for missing data

software

library(ssvd)
library(PMA)
library(softImpute)

settings for the 5 data sets in our paper.

library(R.matlab)
Y_centered = readMat("../data/output/missingdata/GTExZsocre/example.mat")
Y = Y_centered$Ycentered

library(methods)
library(R.matlab)
library(Matrix)
## run the code
ml100K_data = readRDS("../data/output/missingdata/MovieLens/scale_data.rds")
MLMatrix <- sparseMatrix(i = ml100K_data[,1],
                         j = ml100K_data[,2],
                         x = ml100K_data[,3],dims = c(943,1682))
# turn this sparse matrix into matrix in r
Y = as.matrix(MLMatrix)
Y[which(Y == 0)] = NA
# writeMat("~/HG/flash/data/OCVmissflashr2/ML100K_scaled/Ydata.mat", Y = Y)

library(R.matlab)
Y_centered = readMat("../data/output/missingdata/DenoiseRtumor/example.mat")
Y = Y_centered$Ycentered

library(R.matlab)
## run the code
Y_centered = readMat("../data/output/missingdata/DenoiseRtext/example.mat")
Y = Y_centered$Yscaled

library(R.matlab)
## run the code
Y_centered = readMat("../data/output/missingdata/BreastCancer/example.mat")
Y = Y_centered$Y
# in the matlab package of NSF, the use the centered data by rows
N = dim(Y)[1]
P = dim(Y)[2]
Y = Y - rowMeans(Y) %*% t(rep(1,P))

other experiments during the test

library(ggplot2)
plot_res = function(output,title = "data",legend_position = "none", x_label){
  rmse = as.vector(output)
  N = dim(output)[1]
  # methods = rep(c("flash","NBSFA","PMD","softImpute"), each = N)
  methods = rep(x_label, each = N)
  df = data.frame(RMSE = rmse, Method = methods )
  p<-ggplot(df, aes(x=Method, y=RMSE, color=Method)) +
  geom_boxplot()+
  # geom_violin()+
  ggtitle(title) +  theme_bw()+
    theme(legend.position= legend_position, legend.text=element_text(size=15), axis.text.y = element_text(size =6))
  p
}
x_label = c("flash","NBSFA","PMD","softImpute")
PT_res = readRDS("../data/missingvalue/OCVtemplate/RCCres/president_box.rds")
pp = plot_res(PT_res,"Text data",x_label = x_label)
DT_res = readRDS("../data/missingvalue/OCVtemplate/RCCres/denoiseRtumor_box.rds")
pd = plot_res(DT_res,"Tumor data",x_label = x_label)
GZ_res = readRDS("../data/missingvalue/OCVtemplate/RCCres/gtexzscore_box.rds")
pg = plot_res(GZ_res,"GTEx data",x_label = x_label)
BC_res = readRDS("../data/missingvalue/OCVtemplate/RCCres/BreastCancer_box.rds")
pb = plot_res(BC_res,"Breast Cancer data",x_label = x_label)
ML_res = readRDS("../data/missingvalue/OCVtemplate/RCCres/ML100K_box.rds")
ML_res[c(2,13,17,21,29,37,62,76,77,93,95,100),] = NA
ML_res = matrix(as.numeric(ML_res),ncol = 4)
pM = plot_res(ML_res,"Movie Lens data",x_label = x_label)

plots

gridExtra::grid.arrange(pp,pd,pg,pb,pM, layout_matrix = rbind(c(1,NA,2),c(NA,5,NA),c(4,NA,3)))

Warning: Removed 48 rows containing non-finite values (stat_boxplot).

results for different tuning parameters

pmd_c = sapply(seq(1,10),function(x){paste("PMD",x)})
softImpute_c = sapply(seq(1,10),function(x){paste("SF",x)})
x_label= c("flash","NBSFA",pmd_c,softImpute_c)
PT_res = readRDS("../data/missingvalue/box_res_grids_sf_pmd/denoiseTumor_box.rds")
pt = plot_res(PT_res,"Tumor data",x_label = x_label)
pt

PT_res = readRDS("../data/missingvalue/box_res_grids_sf_pmd/TEXT_prsdt_box.rds")
pp = plot_res(PT_res,"Text data",x_label = x_label)
pp

use OCV choose the tuning parameters

PB_res = readRDS("../data/missingvalue/testingcode/box_Breast.rds")
x_label= c("PN","flashG","flashGwn","flashB","nsfa","pmd","soft")
pb = plot_res(PB_res,"Breast Cancer data",x_label = x_label)
pb

x_label= c("PN","flashG","flashGwn","nsfa","pmd","soft")
pb = plot_res(PB_res[,-4],"Breast Cancer data",x_label = x_label)
pb

PT_res = readRDS("../data/missingvalue/testingcode/box_denoiseTumor.rds")
x_label= c("PN","flashG","flashGwn","flashB","nsfa","pmd","soft")
pt = plot_res(PT_res,"Tumor data",x_label = x_label)
pt

PT_res = readRDS("../data/missingvalue/testingcode/box_president.rds")
x_label= c("PN","flashG","flashGwn","flashB","nsfa","pmd","soft","zero")
pt = plot_res(PT_res,"Text data",x_label = x_label)
pt

Session information

sessionInfo()

R version 3.3.0 (2016-05-03)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.13.3 (unknown)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] denoiseR_1.0    scales_0.4.1    MASS_7.3-47     reshape2_1.4.3 
 [5] flashr_0.4-6    workflowr_0.4.0 rmarkdown_1.6   ggplot2_2.2.1  
 [9] R.matlab_3.6.1  softImpute_1.4  Matrix_1.2-11   PMA_1.0.9      
[13] impute_1.48.0   plyr_1.8.4      ssvd_1.0       

loaded via a namespace (and not attached):
 [1] ashr_2.2-3           lattice_0.20-35      colorspace_1.3-2    
 [4] htmltools_0.3.6      yaml_2.1.16          rlang_0.1.6         
 [7] R.oo_1.21.0          withr_2.1.1          R.utils_2.5.0       
[10] foreach_1.4.4        stringr_1.2.0        munsell_0.4.3       
[13] gtable_0.2.0         R.methodsS3_1.7.1    devtools_1.13.3     
[16] codetools_0.2-15     leaps_3.0            evaluate_0.10.1     
[19] memoise_1.1.0        labeling_0.3         knitr_1.18          
[22] pscl_1.5.2           doParallel_1.0.11    irlba_2.2.1         
[25] parallel_3.3.0       curl_2.8.1           Rcpp_0.12.14        
[28] flashClust_1.01-2    backports_1.1.2      scatterplot3d_0.3-40
[31] truncnorm_1.0-7      gridExtra_2.3        digest_0.6.13       
[34] stringi_1.1.6        flashr2_0.4-0        grid_3.3.0          
[37] rprojroot_1.2        tools_3.3.0          magrittr_1.5        
[40] lazyeval_0.2.0       tibble_1.3.4         cluster_2.0.6       
[43] FactoMineR_1.36      SQUAREM_2017.10-1    httr_1.3.0          
[46] rstudioapi_0.6       iterators_1.0.9      R6_2.2.2            
[49] git2r_0.19.0