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Optimizations to R ETL process (esp. loading). #4

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15 changes: 11 additions & 4 deletions code/R_benchmark/generate_Rdata.R
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Original file line number Diff line number Diff line change
@@ -1,3 +1,6 @@
library(data.table)
suppressPackageStartupMessages(library(bit64))

args <- commandArgs(trailingOnly = TRUE)
PATH <- args[1]
NGENES <- args[2]
Expand All @@ -9,14 +12,18 @@ PATIENTS <- paste(PATH, '/PatientMetaData-', NGENES, '-', NPATIENTS, sep="")
TXT <- '.txt'
RDATA <- '.Rdata'

geo <- read.csv(paste(GEO, TXT, sep=""))
geo <- fread(paste(GEO, TXT, sep=""))
save(geo, file=paste(GEO, RDATA, sep=""))
geo <- NULL

go <- read.csv(paste(GO, TXT, sep=""))
go <- fread(paste(GO, TXT, sep=""))
save(go, file=paste(GO, RDATA, sep=""))
go <- NULL

genes <- read.csv(paste(GENES, TXT, sep=""))
genes <- fread(paste(GENES, TXT, sep=""))
save(genes, file=paste(GENES, RDATA, sep=""))
genes <- NULL

patients <- read.csv(paste(PATIENTS, TXT, sep=""))
patients <- fread(paste(PATIENTS, TXT, sep=""))
save(patients, file=paste(PATIENTS, RDATA, sep=""))
patients <- NULL
49 changes: 27 additions & 22 deletions code/R_benchmark/vanilla_R_benchmark.R
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Original file line number Diff line number Diff line change
Expand Up @@ -3,38 +3,43 @@ args <- commandArgs(trailingOnly = TRUE)
PATH <- args[1]
NGENES <- args[2]
NPATIENTS <- args[3]
GEO <- paste(PATH, '/GEO-', NGENES, '-', NPATIENTS, '.Rdata', sep="")
GO <- paste(PATH, '/GO-', NGENES, '-', NPATIENTS, '.Rdata', sep="")
GENES <- paste(PATH, '/GeneMetaData-', NGENES, '-', NPATIENTS, '.Rdata', sep="")
PATIENTS <- paste(PATH, '/PatientMetaData-', NGENES, '-', NPATIENTS, '.Rdata', sep="")
GEO <- paste(PATH, '/GEO-', NGENES, '-', NPATIENTS, '.txt', sep="")
GO <- paste(PATH, '/GO-', NGENES, '-', NPATIENTS, '.txt', sep="")
GENES <- paste(PATH, '/GeneMetaData-', NGENES, '-', NPATIENTS, '.txt', sep="")
PATIENTS <- paste(PATH, '/PatientMetaData-', NGENES, '-', NPATIENTS, '.txt', sep="")

regression <- function()
{
library(Matrix)
library(data.table)
suppressPackageStartupMessages(library(bit64))

ptm = proc.time()

### Data Management ops start ###

load(GEO)
load(GENES)
load(PATIENTS)
geo <- as.data.frame(fread(GEO))
genes <- as.data.frame(fread(GENES))
patients <- as.data.frame(fread(PATIENTS))

sub_gmd = genes[genes$func < 250,]
colnames(genes) <- c("id", "target", "position", "length", "function")
colnames(geo) <- c("geneid", "patientid", "expression value")
colnames(patients) <- c("id", "age", "gender", "zipcode", "disease", "drug response")

sub_gmd = genes[genes$`function` < 250,]

# convert to data tables
colnames(sub_gmd)[1] = "geneid"
sub_gmd_dt = data.table(sub_gmd, key="geneid")
geo_dt = data.table(geo, key="geneid")

# join
A = merge(geo_dt, sub_gmd_dt)[,c("patientid", "geneid", "expression.value"), with=F]
A = merge(geo_dt, sub_gmd_dt)[,c("patientid", "geneid", "expression value"), with=F]

# store as matrix
library(reshape2)
A = acast(A, list(names(A)[1], names(A)[2]));
response = patients[,"drug.response"]
A = acast(A, list(colnames(A)[1], colnames(A)[2]));
response = patients[,"drug response"]

### Data management ops end ###
cat(sprintf('Regression data management: %f\n', (proc.time() - ptm)['elapsed']))
Expand Down Expand Up @@ -68,12 +73,12 @@ covariance <- function()

# join
A = merge(geo_dt, sub_pmd_dt)[,c("patientid", "geneid", "expression.value"), with=F]

# store as matrix
library(reshape2)
A = acast(A, list(names(A)[1], names(A)[2]));
midtm = (proc.time() - ptm)['elapsed']
ptm = proc.time()
ptm = proc.time()

# calculate covariance
covar = cov(A)
Expand All @@ -82,8 +87,8 @@ covariance <- function()

covar <- which(covar>0.01*(max(covar))return, arr.ind=T)
res = merge(covar, gmd_dt, by.x='row', by.y='id')
res = merge(res, gmd_dt, by.x='col', by.y='id')
res = merge(res, gmd_dt, by.x='col', by.y='id')

### Data management ops end ###
cat(sprintf('Regression data management: %f\n', (proc.time() - ptm)['elapsed'] + midtm))
}
Expand All @@ -99,7 +104,7 @@ biclustering<-function()

load(GEO)
load(PATIENTS)

sub_pmd = patients[patients$gender==1 & patients$age<=40,]

# convert to data tables
Expand All @@ -117,13 +122,13 @@ biclustering<-function()
### Data management ops end ###
cat(sprintf('Regression data management: %f\n', (proc.time() - ptm)['elapsed']))
ptm = proc.time()

# run biclustering
library(biclust)
library("s4vd")
biclust(A, method=BCssvd, K=1)
cat(sprintf('Biclust analytics: %f\n', (proc.time() - ptm)['elapsed']))
}
}

svd_irlba <- function()
{
Expand Down Expand Up @@ -187,7 +192,7 @@ stats <- function()
library(reshape2)
A = acast(geo, list(names(geo)[1], names(geo)[2]));
go = sparseMatrix(go[,1], go[,2], x=go[,3])

### Data management ops end ###
cat(sprintf('Stats data management: %f\n', (proc.time() - ptm)['elapsed']))
ptm = proc.time()
Expand All @@ -209,4 +214,4 @@ print(paste('Regression: ', system.time(regression(), gcFirst=T)['elapsed'], sep
print(paste('SVD: ', system.time(svd_irlba(), gcFirst=T)['elapsed'], sep=''));
print(paste('Covariance: ', system.time(covariance(), gcFirst=T)['elapsed'], sep=''));
print(paste('Biclustering: ', system.time(biclustering(), gcFirst=T)['elapsed'], sep=''));
print(paste('Stats: ', system.time(stats(), gcFirst=T)['elapsed'], sep=''));
print(paste('Stats: ', system.time(stats(), gcFirst=T)['elapsed'], sep=''));