examples/voice recognition/voice_bin.R
In aliaksah/EMJMCMC2016: EMJMCMC
#compare on Asteroids
library(RCurl)
library(glmnet)
library(xgboost)
library(h2o)
library(caret)
#define your working directory, where the data files are stored
workdir<-""
source("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/R/the_mode_jumping_package2.r")
#prepare the test set data
voice <- read.table(text = getURL("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/examples/voice%20recognition/voice.csv"),sep = ",",header = T,fill=TRUE)
voice$label<-as.numeric((voice$label=="male"))
gc()
results<-array(0,dim = c(11,100,5))
#GMJMCMC
gant<-function(x,q)as.integer(x<quantile(x,probs = q))
df<-voice$label
for(i in 1:20)
{
for(j in 1:3)
{
df<-cbind(df,gant(voice[,i],0.25*j))
}
}
df<-as.data.frame(df)
names(df)[1]<-"label"
index <- createDataPartition(df$label, p = 0.25, list = FALSE)
test <- df[-index, ]
train <- df[index, ]
data.example <- as.data.frame(train,stringsAsFactors = T)
estimate.bas.glm.cpen <- function(formula, data, family, prior, logn,r = 0.1,yid=1,relat =c("cosi","sigmoid","tanh","atan","erf","m("))
{
#only poisson and binomial families are currently adopted
X <- model.matrix(object = formula,data = data)
capture.output({out <- bayesglm.fit(x = X, y = data[,yid], family=family,coefprior=prior)})
fmla.proc<-as.character(formula)[2:3]
fobserved <- fmla.proc[1]
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
#fparam <-stri_split_fixed(str = fmla.proc[2],pattern = "+I",omit_empty = F)[[1]]
#sj<-(stri_count_fixed(str = fparam, pattern = "("))
sj<-2*(stri_count_fixed(str = fmla.proc[2], pattern = "*"))
sj<-sj+1*(stri_count_fixed(str = fmla.proc[2], pattern = "+"))
for(rel in relat)
sj<-sj+2*(stri_count_fixed(str = fmla.proc[2], pattern = rel))
#sj<-sj+1
mlik = ((-out$deviance +2*log(r)*sum(sj)))/2
#print(sj)
#print(sum(sj))
return(list(mlik = mlik,waic = -(out$deviance + 2*out$rank) , dic = -(out$deviance + logn*out$rank),summary.fixed =list(mean = coefficients(out))))
}
estimate.bas.glm.cpen <- function(formula, data, family, prior, logn,r = 0.1,yid=1)
{
#only poisson and binomial families are currently adopted
X <- model.matrix(object = formula,data = data)
out <- bayesglm.fit(x = X, y = data[,yid], family=family,coefprior=prior)
fmla.proc<-as.character(formula)[2:3]
fobserved <- fmla.proc[1]
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
fparam <-stri_split_fixed(str = fmla.proc[2],pattern = "+I",omit_empty = F)[[1]]
sj<-(stri_count_fixed(str = fparam, pattern = "("))
mlik = (-(out$deviance -2*log(r)*sum(sj)))/2
return(list(mlik = mlik,waic = -(out$deviance + 2*out$rank) , dic = -(out$deviance + logn*out$rank),summary.fixed =list(mean = coefficients(out))))
}
ids<-which((abs(cor(df)[1,]))>0.0)[-1]
h2o.init(nthreads=-1, max_mem_size = "6G")
h2o.removeAll()
cosi<-function(x)cos(x/180*pi)
M<-10
results<-array(0,dim = c(11,M,5))
for(ii in 1:M)
{
print(paste("iteration ",ii))
# capture.output({withRestarts(tryCatch(capture.output({
set.seed(ii)
index <- createDataPartition(df$label, p = 0.25, list = FALSE)
test <- df[-index, ]
train <- df[index, ]
data.example <- as.data.frame(train,stringsAsFactors = T)
t<-system.time({
formula1 = as.formula(paste(colnames(data.example)[1],"~ 1 +",paste0(colnames(data.example)[ids],collapse = "+")))
res = runemjmcmc(formula = formula1,data = data.example,presearch=T, locstop =T,estimator =estimate.bas.glm.cpen,estimator.args = list(data = data.example,prior = aic.prior(),family = binomial(), logn = log(64),r=exp(-1),yid=1),recalc_margin = 95, save.beta = T,interact = T,relations = c("cosi","sigmoid","tanh","atan","erf"),relations.prob =c(0.1,0.1,0.1,0.1,0.1),interact.param=list(allow_offsprings=3,mutation_rate = 500,last.mutation=2500, max.tree.size = 4, Nvars.max =70,p.allow.replace=0.1,p.allow.tree=0.5,p.nor=0.3,p.and = 0.7),n.models = 10000,unique =T,max.cpu = 4,max.cpu.glob = 4,create.table = F,create.hash = T,pseudo.paral = T,burn.in = 100,print.freq = 1000,advanced.param = list(
max.N.glob=as.integer(10),
min.N.glob=as.integer(5),
max.N=as.integer(3),
min.N=as.integer(1),
printable = F))
})
results[1,ii,4]<-t[3]
ppp<-mySearch$post_proceed_results_hash(hashStat = hashStat)
ppp$p.post
mySearch$g.results[,]
mySearch$fparam
g<-function(x)
{
return((x = 1/(1+exp(-x))))
}
Nvars<-mySearch$Nvars
linx <-mySearch$Nvars+4
lHash<-length(hashStat)
mliks <- values(hashStat)[which((1:(lHash * linx)) %% linx == 1)]
betas <- values(hashStat)[which((1:(lHash * linx)) %% linx == 4)]
for(i in 1:(Nvars-1))
{
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (4+i))])
}
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (0))])
t<-system.time({
res<-mySearch$forecast.matrix.na(link.g = g,covariates = (test[,-1]),betas = betas,mliks.in = mliks)$forecast
})
results[1,ii,5]<-t[3]
summary(res)
length(res)
res<-as.integer(res>=0.5)
length(which(res>=0.5))
length(which(res<0.5))
length(res)
length(which(test$label==1))
#(1-sum(abs(res-data.example1$neo),na.rm = T)/20702)
results[1,ii,1]<-(1-sum(abs(res-test$label),na.rm = T)/length(res))
print(results[1,ii,1])
gc()
#FNR
ps<-which(test$label==1)
results[1,ii,2]<-sum(abs(res[ps]-test$label[ps]))/(sum(abs(res[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[1,ii,3]<-sum(abs(res[ns]-test$label[ns]))/(sum(abs(res[ns]-test$label[ns]))+length(ns))
gc()
# })), abort = function(){onerr<-TRUE;out<-NULL})})
# print(results[1,ii,1])
#
}
#MJMCMC
t<-system.time({
formula1 = as.formula(paste(colnames(data.example)[1],"~ 1 +",paste0(colnames(data.example)[-1],collapse = "+")))
res = runemjmcmc(formula = formula1,data = data.example,estimator =estimate.bas.glm,estimator.args = list(data = data.example,prior = aic.prior(),family = binomial(), logn = log(2376)),recalc_margin = 50, save.beta = T,interact = F,relations = c("","lgx2","cos","sigmoid","tanh","atan","erf"),relations.prob =c(0.4,0.0,0.0,0.0,0.0,0.0,0.0),interact.param=list(allow_offsprings=2,last.mutation=1000,mutation_rate = 100, max.tree.size = 200000, Nvars.max = 21,p.allow.replace=0.1,p.allow.tree=0.1,p.nor=0.3,p.and = 0.7),n.models = 2000,unique = T,max.cpu = 4,max.cpu.glob = 4,create.table = F,create.hash = T,pseudo.paral = T,burn.in = 100,print.freq = 1000,advanced.param = list(
max.N.glob=as.integer(10),
min.N.glob=as.integer(5),
max.N=as.integer(3),
min.N=as.integer(1),
printable = F))
})
results[2,ii,4]<-t[3]
ppp<-mySearch$post_proceed_results_hash(hashStat = hashStat)
ppp$p.post
mySearch$g.results[,]
mySearch$fparam
g<-function(x)
{
return((x = 1/(1+exp(-x))))
}
Nvars<-mySearch$Nvars
linx <-mySearch$Nvars+4
lHash<-length(hashStat)
mliks <- values(hashStat)[which((1:(lHash * linx)) %% linx == 1)]
betas <- values(hashStat)[which((1:(lHash * linx)) %% linx == 4)]
for(i in 1:(Nvars-1))
{
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (4+i))])
}
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (0))])
t<-system.time({
res<-mySearch$forecast.matrix.na(link.g = g,covariates = (test[,-1]),betas = betas,mliks.in = mliks)$forecast
})
results[2,ii,5]<-t[3]
summary(res)
length(res)
res<-as.integer(res>=0.5)
length(which(res>=0.5))
length(which(res<0.5))
length(res)
length(which(test$label==1))
#(1-sum(abs(res-data.example1$neo),na.rm = T)/20702)
results[2,ii,1]<-(1-sum(abs(res-test$label),na.rm = T)/length(res))
print(results[2,ii,1])
gc()
#FNR
ps<-which(test$label==1)
results[2,ii,2]<-sum(abs(res[ps]-test$label[ps]))/(sum(abs(res[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[2,ii,3]<-sum(abs(res[ns]-test$label[ns]))/(sum(abs(res[ns]-test$label[ns]))+length(ns))
gc()
#xGboost AUC gbtree
t<-system.time({
param <- list(objective = "binary:logistic",
eval_metric = "auc",
booster = "gbtree",
eta = 0.05,
subsample = 0.86,
colsample_bytree = 0.92,
colsample_bylevel = 0.9,
min_child_weight = 0,
gamma = 0.005,
max_depth = 15)
# train<-as.data.frame(data.example[,-c(21)])
# test<-as.data.frame(test[,-c(21)])
dval<-xgb.DMatrix(data = data.matrix(train[,-1]), label = data.matrix(train[,1]),missing=NA)
watchlist<-list(dval=dval)
m2 <- xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-1]), label = data.matrix(train[,1]),missing=NA),
param, nrounds = 1000,
watchlist = watchlist,
print_every_n = 1000)
})
# Predict
results[3,ii,4]<-t[3]
t<-system.time({
dtest <- xgb.DMatrix(data.matrix(test[,-1]),missing=NA)
})
t<-system.time({
out <- predict(m2, dtest)
})
results[3,ii,5]<-t[3]
out<-as.integer(out>=0.5)
print( results[3,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[3,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[3,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#xGboost AUC gblinear
t<- system.time({
param <- list(objective = "binary:logistic",
eval_metric = "auc",
booster = "gblinear",
eta = 0.05,
subsample = 0.86,
colsample_bytree = 0.92,
colsample_bylevel = 0.9,
min_child_weight = 0,
gamma = 0.005,
max_depth = 15)
as.h2o(test[,-1])
dval<-xgb.DMatrix(data = data.matrix(train[,-1]), label = data.matrix(train[,1]),missing=NA)
watchlist<-list(dval=dval)
m2 <- xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-1]), label = data.matrix(train[,1]),missing=NA),
param, nrounds = 1000,
watchlist = watchlist,
print_every_n = 1000)
})
# Predict
results[4,ii,4]<-t[3]
t<-system.time({
dtest <- xgb.DMatrix(data.matrix(test[,-1]),missing=NA)
})
t<-system.time({
out <- predict(m2, dtest)
})
results[4,ii,5]<-t[3]
out<-as.integer(out>=0.5)
print(results[4,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[4,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[4,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#GLMNET (elastic networks) # lasso a=1
t<-system.time({
fit2 <- glmnet(as.matrix(train)[,-1], train$label, family="binomial")
})
results[5,ii,4]<-t[3]
mmm<-as.matrix(test[,-1])
mmm[which(is.na(mmm))]<-0
t<-system.time({
out <- predict(fit2,mmm , type = "response")[,fit2$dim[2]]
})
results[5,ii,5]<-t[3]
out<-as.integer(out>=0.5)
print(results[5,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[5,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[5,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
# ridge a=0
t<-system.time({
fit2 <- glmnet(as.matrix(train)[,-1], train$label, family="binomial",alpha=0)
})
results[6,ii,4]<-t[3]
mmm<-as.matrix(test[,-1])
mmm[which(is.na(mmm))]<-0
t<-system.time({
out <- predict(fit2,mmm , type = "response")[,fit2$dim[2]]
})
results[6,ii,5]<-t[3]
out<-as.integer(out>=0.5)
print(results[6,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[6,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[6,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
gc()
# h2o.random forest
df <- as.h2o(train)
train1 <- h2o.assign(df , "train1.hex")
valid1 <- h2o.assign(df , "valid1.hex")
test1 <- h2o.assign(as.h2o(test[,-1]), "test1.hex")
train1[1:5,]
features = names(train1)[-1]
# in order to make the classification prediction
train1$label <- as.factor(train1$label)
t<-system.time({
rf1 <- h2o.randomForest( stopping_metric = "AUC",
training_frame = train1,
validation_frame = valid1,
x=features,
y="label",
model_id = "rf1",
ntrees = 10000,
stopping_rounds = 3,
score_each_iteration = T,
ignore_const_cols = T,
seed = ii)
})
results[7,ii,4]<-t[3]
t<-system.time({
out<-h2o.predict(rf1,as.h2o(test1))[,1]
})
results[7,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[7,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[7,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[7,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#h2o deeplearning
t<-system.time({
neo.dl <- h2o.deeplearning(x = features, y = "label",hidden=c(200,200,200,200,200,200),
distribution = "bernoulli",
training_frame = train1,
validation_frame = valid1,
seed = ii)
})
# now make a prediction
results[8,ii,4]<-t[3]
t<-system.time({
out<-h2o.predict(neo.dl,as.h2o(test1))[,1]
})
results[8,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[8,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[8,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[8,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#h2o glm
t<-system.time({
neo.glm <- h2o.glm(x = features, y = "label",
family = "binomial",
training_frame = train1,
validation_frame = valid1,
#lambda = 0,
#alpha = 0,
lambda_search = F,
seed = ii)
})
# now make a prediction
results[9,ii,4]<-t[3]
t<-system.time({
out<-h2o.predict(neo.glm,as.h2o(test1))[,1]
})
results[9,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[9,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[9,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[9,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#h2o naive bayes
t<-system.time({
neo.nb <- h2o.naiveBayes(x = features, y = "label",
training_frame = train1,
validation_frame = valid1,
seed = ii)
})
# now make a prediction
results[10,ii,4]<-t[3]
t<-system.time({
out<-h2o.predict(neo.nb,as.h2o(test1))[,1]
})
results[10,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[10,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[10,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[10,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#h2o kmeans
t<-system.time({
neo.nb <- h2o.kmeans(x = c(features,"label"),k=2,
training_frame = train1,
seed = ii)
})
results[11,ii,4]<-t[3]
# now make a prediction
test2 <- h2o.assign(as.h2o(test), "test2.hex")
t<-system.time({
out<-h2o.predict(neo.nb,as.h2o(test2))[,1]
})
results[11,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[11,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[11,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[11,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
gc()
})), abort = function(){onerr<-TRUE;out<-NULL})})
print(results[,ii,1])
}
ids<-NULL
for(i in 1:100)
{
if(min(results[,i,1])>0)
ids<-c(ids,i)
}
length(ids)
ress<-results[,ids[1:100],]
summary.results<-array(data = NA,dim = c(11,15))
for(i in 1:11)
{
for(j in 1:5)
{
summary.results[i,(j-1)*3+1]<-min(ress[i,,j])
summary.results[i,(j-1)*3+2]<-median(ress[i,,j])
summary.results[i,(j-1)*3+3]<-max(ress[i,,j])
}
}
summary.results<-as.data.frame(summary.results)
names(summary.results)<-c("min(prec)","median(prec)","max(prec)","min(fnr)","median(fnr)","max(fnr)","min(fpr)","median(fpr)","max(fpr)","min(ltime)","median(ltime)","max(ltime)","min(ptime)","median(ptime)","max(ptime)")
rownames(summary.results)<-c("GMJMCMC(AIC)","MJMCMC(AIC)","tXGBOOST(AUC)","lXGBOOST(AUC)","LASSO","RIDGE","RFOREST","DEEPNETS","NAIVEBAYESS","LR","KMEANS")
for(i in 1:11)
{
plot(density(ress[i,,1],bw = "SJ"), main="Compare Kernel Density of precisions")
polygon(density(ress[i,,1],bw = "SJ"), col="red", border="blue")
}
write.csv(x = round(summary.results,4),file = "/mn/sarpanitu/ansatte-u2/aliaksah/Desktop/package/EMJMCMC/examples/voice recognition/voicerec.csv")
aliaksah/EMJMCMC2016 documentation built on July 27, 2023, 5:48 a.m.
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#compare on Asteroids
library(RCurl)
library(glmnet)
library(xgboost)
library(h2o)
library(caret)
#define your working directory, where the data files are stored
workdir<-""
source("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/R/the_mode_jumping_package2.r")
#prepare the test set data
voice <- read.table(text = getURL("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/examples/voice%20recognition/voice.csv"),sep = ",",header = T,fill=TRUE)
voice$label<-as.numeric((voice$label=="male"))
gc()
results<-array(0,dim = c(11,100,5))
#GMJMCMC
gant<-function(x,q)as.integer(x<quantile(x,probs = q))
df<-voice$label
for(i in 1:20)
{
for(j in 1:3)
{
df<-cbind(df,gant(voice[,i],0.25*j))
}
}
df<-as.data.frame(df)
names(df)[1]<-"label"
index <- createDataPartition(df$label, p = 0.25, list = FALSE)
test <- df[-index, ]
train <- df[index, ]
data.example <- as.data.frame(train,stringsAsFactors = T)
estimate.bas.glm.cpen <- function(formula, data, family, prior, logn,r = 0.1,yid=1,relat =c("cosi","sigmoid","tanh","atan","erf","m("))
{
#only poisson and binomial families are currently adopted
X <- model.matrix(object = formula,data = data)
capture.output({out <- bayesglm.fit(x = X, y = data[,yid], family=family,coefprior=prior)})
fmla.proc<-as.character(formula)[2:3]
fobserved <- fmla.proc[1]
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
#fparam <-stri_split_fixed(str = fmla.proc[2],pattern = "+I",omit_empty = F)[[1]]
#sj<-(stri_count_fixed(str = fparam, pattern = "("))
sj<-2*(stri_count_fixed(str = fmla.proc[2], pattern = "*"))
sj<-sj+1*(stri_count_fixed(str = fmla.proc[2], pattern = "+"))
for(rel in relat)
sj<-sj+2*(stri_count_fixed(str = fmla.proc[2], pattern = rel))
#sj<-sj+1
mlik = ((-out$deviance +2*log(r)*sum(sj)))/2
#print(sj)
#print(sum(sj))
return(list(mlik = mlik,waic = -(out$deviance + 2*out$rank) , dic = -(out$deviance + logn*out$rank),summary.fixed =list(mean = coefficients(out))))
}
estimate.bas.glm.cpen <- function(formula, data, family, prior, logn,r = 0.1,yid=1)
{
#only poisson and binomial families are currently adopted
X <- model.matrix(object = formula,data = data)
out <- bayesglm.fit(x = X, y = data[,yid], family=family,coefprior=prior)
fmla.proc<-as.character(formula)[2:3]
fobserved <- fmla.proc[1]
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = " ",replacement = "")
fmla.proc[2]<-stri_replace_all(str = fmla.proc[2],fixed = "\n",replacement = "")
fparam <-stri_split_fixed(str = fmla.proc[2],pattern = "+I",omit_empty = F)[[1]]
sj<-(stri_count_fixed(str = fparam, pattern = "("))
mlik = (-(out$deviance -2*log(r)*sum(sj)))/2
return(list(mlik = mlik,waic = -(out$deviance + 2*out$rank) , dic = -(out$deviance + logn*out$rank),summary.fixed =list(mean = coefficients(out))))
}
ids<-which((abs(cor(df)[1,]))>0.0)[-1]
h2o.init(nthreads=-1, max_mem_size = "6G")
h2o.removeAll()
cosi<-function(x)cos(x/180*pi)
M<-10
results<-array(0,dim = c(11,M,5))
for(ii in 1:M)
{
print(paste("iteration ",ii))
# capture.output({withRestarts(tryCatch(capture.output({
set.seed(ii)
index <- createDataPartition(df$label, p = 0.25, list = FALSE)
test <- df[-index, ]
train <- df[index, ]
data.example <- as.data.frame(train,stringsAsFactors = T)
t<-system.time({
formula1 = as.formula(paste(colnames(data.example)[1],"~ 1 +",paste0(colnames(data.example)[ids],collapse = "+")))
res = runemjmcmc(formula = formula1,data = data.example,presearch=T, locstop =T,estimator =estimate.bas.glm.cpen,estimator.args = list(data = data.example,prior = aic.prior(),family = binomial(), logn = log(64),r=exp(-1),yid=1),recalc_margin = 95, save.beta = T,interact = T,relations = c("cosi","sigmoid","tanh","atan","erf"),relations.prob =c(0.1,0.1,0.1,0.1,0.1),interact.param=list(allow_offsprings=3,mutation_rate = 500,last.mutation=2500, max.tree.size = 4, Nvars.max =70,p.allow.replace=0.1,p.allow.tree=0.5,p.nor=0.3,p.and = 0.7),n.models = 10000,unique =T,max.cpu = 4,max.cpu.glob = 4,create.table = F,create.hash = T,pseudo.paral = T,burn.in = 100,print.freq = 1000,advanced.param = list(
max.N.glob=as.integer(10),
min.N.glob=as.integer(5),
max.N=as.integer(3),
min.N=as.integer(1),
printable = F))
})
results[1,ii,4]<-t[3]
ppp<-mySearch$post_proceed_results_hash(hashStat = hashStat)
ppp$p.post
mySearch$g.results[,]
mySearch$fparam
g<-function(x)
{
return((x = 1/(1+exp(-x))))
}
Nvars<-mySearch$Nvars
linx <-mySearch$Nvars+4
lHash<-length(hashStat)
mliks <- values(hashStat)[which((1:(lHash * linx)) %% linx == 1)]
betas <- values(hashStat)[which((1:(lHash * linx)) %% linx == 4)]
for(i in 1:(Nvars-1))
{
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (4+i))])
}
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (0))])
t<-system.time({
res<-mySearch$forecast.matrix.na(link.g = g,covariates = (test[,-1]),betas = betas,mliks.in = mliks)$forecast
})
results[1,ii,5]<-t[3]
summary(res)
length(res)
res<-as.integer(res>=0.5)
length(which(res>=0.5))
length(which(res<0.5))
length(res)
length(which(test$label==1))
#(1-sum(abs(res-data.example1$neo),na.rm = T)/20702)
results[1,ii,1]<-(1-sum(abs(res-test$label),na.rm = T)/length(res))
print(results[1,ii,1])
gc()
#FNR
ps<-which(test$label==1)
results[1,ii,2]<-sum(abs(res[ps]-test$label[ps]))/(sum(abs(res[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[1,ii,3]<-sum(abs(res[ns]-test$label[ns]))/(sum(abs(res[ns]-test$label[ns]))+length(ns))
gc()
# })), abort = function(){onerr<-TRUE;out<-NULL})})
# print(results[1,ii,1])
#
}
#MJMCMC
t<-system.time({
formula1 = as.formula(paste(colnames(data.example)[1],"~ 1 +",paste0(colnames(data.example)[-1],collapse = "+")))
res = runemjmcmc(formula = formula1,data = data.example,estimator =estimate.bas.glm,estimator.args = list(data = data.example,prior = aic.prior(),family = binomial(), logn = log(2376)),recalc_margin = 50, save.beta = T,interact = F,relations = c("","lgx2","cos","sigmoid","tanh","atan","erf"),relations.prob =c(0.4,0.0,0.0,0.0,0.0,0.0,0.0),interact.param=list(allow_offsprings=2,last.mutation=1000,mutation_rate = 100, max.tree.size = 200000, Nvars.max = 21,p.allow.replace=0.1,p.allow.tree=0.1,p.nor=0.3,p.and = 0.7),n.models = 2000,unique = T,max.cpu = 4,max.cpu.glob = 4,create.table = F,create.hash = T,pseudo.paral = T,burn.in = 100,print.freq = 1000,advanced.param = list(
max.N.glob=as.integer(10),
min.N.glob=as.integer(5),
max.N=as.integer(3),
min.N=as.integer(1),
printable = F))
})
results[2,ii,4]<-t[3]
ppp<-mySearch$post_proceed_results_hash(hashStat = hashStat)
ppp$p.post
mySearch$g.results[,]
mySearch$fparam
g<-function(x)
{
return((x = 1/(1+exp(-x))))
}
Nvars<-mySearch$Nvars
linx <-mySearch$Nvars+4
lHash<-length(hashStat)
mliks <- values(hashStat)[which((1:(lHash * linx)) %% linx == 1)]
betas <- values(hashStat)[which((1:(lHash * linx)) %% linx == 4)]
for(i in 1:(Nvars-1))
{
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (4+i))])
}
betas<-cbind(betas,values(hashStat)[which((1:(lHash * linx)) %% linx == (0))])
t<-system.time({
res<-mySearch$forecast.matrix.na(link.g = g,covariates = (test[,-1]),betas = betas,mliks.in = mliks)$forecast
})
results[2,ii,5]<-t[3]
summary(res)
length(res)
res<-as.integer(res>=0.5)
length(which(res>=0.5))
length(which(res<0.5))
length(res)
length(which(test$label==1))
#(1-sum(abs(res-data.example1$neo),na.rm = T)/20702)
results[2,ii,1]<-(1-sum(abs(res-test$label),na.rm = T)/length(res))
print(results[2,ii,1])
gc()
#FNR
ps<-which(test$label==1)
results[2,ii,2]<-sum(abs(res[ps]-test$label[ps]))/(sum(abs(res[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[2,ii,3]<-sum(abs(res[ns]-test$label[ns]))/(sum(abs(res[ns]-test$label[ns]))+length(ns))
gc()
#xGboost AUC gbtree
t<-system.time({
param <- list(objective = "binary:logistic",
eval_metric = "auc",
booster = "gbtree",
eta = 0.05,
subsample = 0.86,
colsample_bytree = 0.92,
colsample_bylevel = 0.9,
min_child_weight = 0,
gamma = 0.005,
max_depth = 15)
# train<-as.data.frame(data.example[,-c(21)])
# test<-as.data.frame(test[,-c(21)])
dval<-xgb.DMatrix(data = data.matrix(train[,-1]), label = data.matrix(train[,1]),missing=NA)
watchlist<-list(dval=dval)
m2 <- xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-1]), label = data.matrix(train[,1]),missing=NA),
param, nrounds = 1000,
watchlist = watchlist,
print_every_n = 1000)
})
# Predict
results[3,ii,4]<-t[3]
t<-system.time({
dtest <- xgb.DMatrix(data.matrix(test[,-1]),missing=NA)
})
t<-system.time({
out <- predict(m2, dtest)
})
results[3,ii,5]<-t[3]
out<-as.integer(out>=0.5)
print( results[3,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[3,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[3,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#xGboost AUC gblinear
t<- system.time({
param <- list(objective = "binary:logistic",
eval_metric = "auc",
booster = "gblinear",
eta = 0.05,
subsample = 0.86,
colsample_bytree = 0.92,
colsample_bylevel = 0.9,
min_child_weight = 0,
gamma = 0.005,
max_depth = 15)
as.h2o(test[,-1])
dval<-xgb.DMatrix(data = data.matrix(train[,-1]), label = data.matrix(train[,1]),missing=NA)
watchlist<-list(dval=dval)
m2 <- xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-1]), label = data.matrix(train[,1]),missing=NA),
param, nrounds = 1000,
watchlist = watchlist,
print_every_n = 1000)
})
# Predict
results[4,ii,4]<-t[3]
t<-system.time({
dtest <- xgb.DMatrix(data.matrix(test[,-1]),missing=NA)
})
t<-system.time({
out <- predict(m2, dtest)
})
results[4,ii,5]<-t[3]
out<-as.integer(out>=0.5)
print(results[4,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[4,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[4,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#GLMNET (elastic networks) # lasso a=1
t<-system.time({
fit2 <- glmnet(as.matrix(train)[,-1], train$label, family="binomial")
})
results[5,ii,4]<-t[3]
mmm<-as.matrix(test[,-1])
mmm[which(is.na(mmm))]<-0
t<-system.time({
out <- predict(fit2,mmm , type = "response")[,fit2$dim[2]]
})
results[5,ii,5]<-t[3]
out<-as.integer(out>=0.5)
print(results[5,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[5,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[5,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
# ridge a=0
t<-system.time({
fit2 <- glmnet(as.matrix(train)[,-1], train$label, family="binomial",alpha=0)
})
results[6,ii,4]<-t[3]
mmm<-as.matrix(test[,-1])
mmm[which(is.na(mmm))]<-0
t<-system.time({
out <- predict(fit2,mmm , type = "response")[,fit2$dim[2]]
})
results[6,ii,5]<-t[3]
out<-as.integer(out>=0.5)
print(results[6,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[6,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[6,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
gc()
# h2o.random forest
df <- as.h2o(train)
train1 <- h2o.assign(df , "train1.hex")
valid1 <- h2o.assign(df , "valid1.hex")
test1 <- h2o.assign(as.h2o(test[,-1]), "test1.hex")
train1[1:5,]
features = names(train1)[-1]
# in order to make the classification prediction
train1$label <- as.factor(train1$label)
t<-system.time({
rf1 <- h2o.randomForest( stopping_metric = "AUC",
training_frame = train1,
validation_frame = valid1,
x=features,
y="label",
model_id = "rf1",
ntrees = 10000,
stopping_rounds = 3,
score_each_iteration = T,
ignore_const_cols = T,
seed = ii)
})
results[7,ii,4]<-t[3]
t<-system.time({
out<-h2o.predict(rf1,as.h2o(test1))[,1]
})
results[7,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[7,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[7,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[7,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#h2o deeplearning
t<-system.time({
neo.dl <- h2o.deeplearning(x = features, y = "label",hidden=c(200,200,200,200,200,200),
distribution = "bernoulli",
training_frame = train1,
validation_frame = valid1,
seed = ii)
})
# now make a prediction
results[8,ii,4]<-t[3]
t<-system.time({
out<-h2o.predict(neo.dl,as.h2o(test1))[,1]
})
results[8,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[8,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[8,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[8,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#h2o glm
t<-system.time({
neo.glm <- h2o.glm(x = features, y = "label",
family = "binomial",
training_frame = train1,
validation_frame = valid1,
#lambda = 0,
#alpha = 0,
lambda_search = F,
seed = ii)
})
# now make a prediction
results[9,ii,4]<-t[3]
t<-system.time({
out<-h2o.predict(neo.glm,as.h2o(test1))[,1]
})
results[9,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[9,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[9,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[9,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#h2o naive bayes
t<-system.time({
neo.nb <- h2o.naiveBayes(x = features, y = "label",
training_frame = train1,
validation_frame = valid1,
seed = ii)
})
# now make a prediction
results[10,ii,4]<-t[3]
t<-system.time({
out<-h2o.predict(neo.nb,as.h2o(test1))[,1]
})
results[10,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[10,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[10,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[10,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
#h2o kmeans
t<-system.time({
neo.nb <- h2o.kmeans(x = c(features,"label"),k=2,
training_frame = train1,
seed = ii)
})
results[11,ii,4]<-t[3]
# now make a prediction
test2 <- h2o.assign(as.h2o(test), "test2.hex")
t<-system.time({
out<-h2o.predict(neo.nb,as.h2o(test2))[,1]
})
results[11,ii,5]<-t[3]
out<-as.data.frame(out)
out<-as.integer(as.numeric(as.character(out$predict)))
print(results[11,ii,1]<-(1-sum(abs(out-test$label[1:length(out)]))/length(out)))
#FNR
ps<-which(test$label==1)
results[11,ii,2]<-sum(abs(out[ps]-test$label[ps]))/(sum(abs(out[ps]-test$label[ps]))+length(ps))
#FPR
ns<-which(test$label==0)
results[11,ii,3]<-sum(abs(out[ns]-test$label[ns]))/(sum(abs(out[ns]-test$label[ns]))+length(ns))
gc()
})), abort = function(){onerr<-TRUE;out<-NULL})})
print(results[,ii,1])
}
ids<-NULL
for(i in 1:100)
{
if(min(results[,i,1])>0)
ids<-c(ids,i)
}
length(ids)
ress<-results[,ids[1:100],]
summary.results<-array(data = NA,dim = c(11,15))
for(i in 1:11)
{
for(j in 1:5)
{
summary.results[i,(j-1)*3+1]<-min(ress[i,,j])
summary.results[i,(j-1)*3+2]<-median(ress[i,,j])
summary.results[i,(j-1)*3+3]<-max(ress[i,,j])
}
}
summary.results<-as.data.frame(summary.results)
names(summary.results)<-c("min(prec)","median(prec)","max(prec)","min(fnr)","median(fnr)","max(fnr)","min(fpr)","median(fpr)","max(fpr)","min(ltime)","median(ltime)","max(ltime)","min(ptime)","median(ptime)","max(ptime)")
rownames(summary.results)<-c("GMJMCMC(AIC)","MJMCMC(AIC)","tXGBOOST(AUC)","lXGBOOST(AUC)","LASSO","RIDGE","RFOREST","DEEPNETS","NAIVEBAYESS","LR","KMEANS")
for(i in 1:11)
{
plot(density(ress[i,,1],bw = "SJ"), main="Compare Kernel Density of precisions")
polygon(density(ress[i,,1],bw = "SJ"), col="red", border="blue")
}
write.csv(x = round(summary.results,4),file = "/mn/sarpanitu/ansatte-u2/aliaksah/Desktop/package/EMJMCMC/examples/voice recognition/voicerec.csv")
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