Created
September 30, 2013 06:28
-
-
Save xiaodaigh/6760005 to your computer and use it in GitHub Desktop.
pamp gini
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# | |
# | |
cor.ilic <- function(x,y) { | |
# tryCatch(t <- table(x,y,exclude=NULL,useNA="ifany") | |
# , error = function(e) { | |
# | |
# } | |
# ) | |
bt <- table.p(data.frame(x=x,y=y),32) | |
#t <- reshape(bt,idvar=names(bt)[1],timevar=names(bt)[2],direction="wide") #takes too fucking long | |
t <- bt$Freq | |
sumt <- sum(t) | |
t1 <- t / sumt | |
hxy <- sum(-t1 * ifelse(t1==0,0,log(t1))) | |
#x.margin <- apply(t1,1,sum) | |
#y.margin <- apply(t1,2,sum) | |
x.margin <- aggregate(bt$Freq/sumt,by=list(addNA(as.factor(bt[[1]]))),sum)$x | |
y.margin <- aggregate(bt$Freq/sumt,by=list(addNA(as.factor(bt[[2]]))),sum)$x | |
hx <- sum(-x.margin * ifelse(x.margin==0,0,log(x.margin))) | |
hy <- sum(-y.margin * ifelse(y.margin==0,0,log(y.margin))) | |
(hx + hy - hxy) / min(hx,hy) | |
} | |
z = d$default | |
x = d1$AVG_CASA_AGG_BAL_L12MWs | |
y = d1$UTL_OD_L12MWs | |
x1 = x[z==1] | |
y1 = y[z==1] | |
cor.ilic(x,y) | |
x0 = x[z==0] | |
y0 = x[z==0] | |
x = runif(length(d3$UTL_OD_L12M)) | |
y = runif(length(d3$UTL_OD_L12M)) | |
d3$AVG_CASA_AGG_BAL_L12M | |
x <- d3$AVG_CASA_BAL_L6MW | |
y <- d3$AVG_CASA_AGG_BAL_L12MW | |
y = runif(length(unique(d3$AVG_CASA_BAL_L6MW))) | |
x = runif(length(unique(d3$AVG_CASA_BAL_L6MW))) | |
cor.ilic(x,y) | |
d4 <- subset(d3,select=c("AVG_CASA_AGG_BAL_L12M","AVG_CASA_BAL_L6M")) | |
system.time(bt <- table.p(d,chunks=32)) | |
#require(bigtabulate) | |
cor.ilic(x,y) | |
#d2 <- subset(d1,z == 0) | |
d2 <-data | |
all.pairs <- combn(names(d2),2) | |
icorr <- apply(all.pairs,2,function(n2) {xy <- d2[n2]; x<-xy[[1]]; y<-xy[[2]];cor.ilic(x,y)}) | |
ns <- apply(all.pairs,2,function(n2) {paste(n2,collapse="+")}) | |
names(icorr) <- ns | |
icorr <- sort(icorr,decreasing=TRUE) | |
icorr.df <- as.data.frame(icorr) | |
fix(icorr.df) | |
#d1[all.pairs[,1]] | |
t <- table(x,y,z,exclude=NULL,useNA="ifany") | |
t1 <- t / sum(t) | |
hxy <- sum(-t1 * ifelse(t1==0,0,log(t1))) | |
x.margin <- apply(t1,1,sum) | |
y.margin <- apply(t1,2,sum) | |
z.margin <- apply(t1,3,sum) | |
hx <- sum(-x.margin * ifelse(x.margin==0,0,log(x.margin))) | |
hy <- sum(-y.margin * ifelse(y.margin==0,0,log(y.margin))) | |
hz <- sum(-z.margin * ifelse(z.margin==0,0,log(z.margin))) | |
ilic.corr <- (hx + hy + hz - hxy) / min(hx,hy,hz) | |
set.seed(3) | |
system.time(s <- replicate(10000,diff(c(0,sort(sample(seq(0,1,0.01),9,replace=TRUE)),1)))) | |
which(apply(s,2,sum)==1) | |
all(apply(s,2,sum)-1 < 0.001) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
# d <- read.csv("c:/temp/gini.csv") | |
# pred <- d$score | |
# actual <- d$bad_outcome | |
d3 <- read.csv("c:/temp/datalah.csv") | |
d <- subset(d3,select=c("AVG_CASA_AGG_BAL_L12M","AVG_CASA_BAL_L6M")) | |
d <- subset(d3,select=c("AVG_CASA_AGG_BAL_L12M","default")) | |
bt <- table.p(d,chunks=32) | |
#paralellized table | |
table.p <- function(d,chunks=NULL) { | |
require(parallel) | |
require(doSNOW) | |
# determine the number of workers available | |
n.threads <- parallel::detectCores() | |
if (is.null(chunks)) chunks <- n.threads | |
#grouping <- rep_len(1:n.threads,length(d[[1]])) | |
grouping <- rep(1:chunks,length.out=length(d[[1]])) | |
#doing multiple tables | |
print("multiple table") | |
haha <- foreach(i = 1:chunks) %dopar% ( | |
table(d[grouping==i,],exclude=NULL, useNA ="always") | |
) | |
print("FINISHED: multiple tables") | |
base = NULL | |
print("merging datasets") | |
for (i in 1:chunks) { | |
ddd <- data.frame(haha[[i]]) | |
names.l <- length(names(ddd)) | |
names(ddd) <- c(names(ddd)[-names.l],"Freq") | |
ddd <- subset(ddd,Freq!=0) #for sparse matrix this will vastly improve the performance | |
names(ddd) <- c(names(ddd)[-names.l],paste("Freq",i)) | |
if (i==1) { | |
base <- ddd | |
} | |
else base <- merge(base,ddd,all=TRUE) | |
print(paste(i,chunks,sep=" out of ")) | |
gc() | |
} | |
print("FINISHED: merging datasets") | |
print("Creating Freq") | |
freq.i <- base[[names.l - 1 +1]] | |
base$Freq <- ifelse(is.na(freq.i),rep(0,length(freq.i)),freq.i) | |
for (i in 2:chunks) { | |
freq.i <- base[[names.l - 1 +i]] | |
base$Freq <- base$Freq + ifelse(is.na(freq.i),rep(0,length(freq.i)),freq.i) | |
#print(i) | |
} | |
print("FINISHED: Creating Freq") | |
#clean up | |
print("clean up") | |
for (i in 1:chunks) { | |
base[[names.l - 1 +1]] <- NULL | |
} | |
print("FINISHED: clean up") | |
# | |
return(base) | |
} | |
# it is assumed that d has a number of data | |
gini.pamp <- function(pred, actual,chunks=NULL .parallel = FALSE) { | |
if (.parallel) { | |
require(parallel) | |
require(doSNOW) | |
# determine the number of workers available | |
n.threads <- getDoParWorkers() | |
} | |
# compute the table first | |
if (.parallel) { | |
system.time(table.d1 <- acast(table.p(data.frame(pred=pred,actual=actual)),pred~actual,sum,value.var="Freq")) | |
table.d <- table.d1[order(as.numeric(dimnames(table.d1)[[1]])),] | |
} | |
else | |
system.time(table.d <- table(data.frame(pred=pred,actual=actual))) | |
#print("cumsum") | |
# cumsum | |
good.cumsum <- cumsum(table.d[,1]) / sum(table.d[,1]) | |
bad.cumsum <- cumsum(table.d[,2]) / sum(table.d[,2]) | |
tmp.l <- length(good.cumsum) | |
#1 - sum(diff(bad.cumsum) * (good.cumsum[-1] + good.cumsum[-tmp.l])) | |
1 - crossprod(diff(bad.cumsum) , (good.cumsum[-1] + good.cumsum[-tmp.l])) | |
# divide the data up into threads and compute the Gini | |
} | |
#system.time(print(gini.pamp(d$score, d$bad_outcome, .parallel = TRUE))) | |
#system.time(print(gini.pamp(d$score, d$bad_outcome))) | |
gc() | |
x <- runif(2^23) | |
y <- ifelse(runif(2^23)<0.5,0,1) | |
#x <- runif(100000) | |
#y <- ifelse(runif(100000)<0.5,0,1) | |
system.time(print(gini.pamp(x, y))) | |
# the package that allows for parallel processing | |
require(parallel) | |
require(doSNOW) | |
require(reshape2) | |
# i have 8 cpu cores | |
cores = 2 | |
# some generic code to tell R to use the 8 cores | |
#browser() | |
cl = makeCluster(min(8,cores)) | |
registerDoSNOW(cl) | |
getDoParWorkers() | |
system.time(print(gini.pamp(x, y, .parallel = TRUE))) | |
#seems like merging takes the longest time | |
stopCluster(cl) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment