supplementaries/BGNLM/spam/spam_competitors.r
In aliaksah/EMJMCMC2016: EMJMCMC
#load some required libraries
library(RCurl)
library(glmnet)
library(xgboost)
library(h2o)
library(BAS)
library(caret)
#define your working directory, where the data files are stored
workdir=""
#read in the train and test data sets
data = read.table("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/supplementaries/BGNLM/spam/spam.data",col.names=c(paste("x",1:57,sep=""),"X"))
data[,1:57] = scale(data[,1:57])
set.seed(1)
spam.traintest = read.table("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/supplementaries/BGNLM/spam/spam.traintest")#rbinom(n = dim(data)[1],size = 1,prob = 0.01)
train = data[spam.traintest==1,]
test = data[spam.traintest==0,]
data.example = train
#prepare the data structures for the final results
results=array(0,dim = c(11,100,5))
#GMJMCMC
# h2o initialize
h2o.init(nthreads=-1, max_mem_size = "6G")
h2o.removeAll()
for(ii in 1:100)
{
print(paste("iteration ",ii))
capture.output({withRestarts(tryCatch(capture.output({
#here we are no longer running DBRM, since DBRM algorithms are run via other scripts
#for computational efficiency and speed
set.seed(ii)
#xGboost logloss gblinear
t=system.time({
param = list(objective = "binary:logistic",
eval_metric = "logloss",
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)
dval=xgb.DMatrix(data = data.matrix(train[,-58]), label = data.matrix(train[,58]),missing=NA)
watchlist=list(dval=dval)
m2 = xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-58]), label = data.matrix(train[,58]),missing=NA),
param, nrounds = 10000,
watchlist = watchlist,
print_every_n = 10)
})
# Predict
results[3,ii,4]=t[3]
t=system.time({
dtest = xgb.DMatrix(data.matrix(test[,-58]),missing=NA)
})
t=system.time({
out = predict(m2, dtest)
})
results[3,ii,5]=t[3]
out=as.integer(out>=0.5)
#compute and store the performance metrics
print( results[3,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[3,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[3,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
# xgboost logLik gbtree
t=system.time({
param = list(objective = "binary:logistic",
eval_metric = "logloss",
booster = "gbtree",
eta = 0.05,
subsample = 0.86,
colsample_bytree = 0.92,
colsample_bylevel = 0.9,
min_child_weight = 0,
gamma = 0.005,
max_depth = 15)
dval=xgb.DMatrix(data = data.matrix(train[,-58]), label = data.matrix(train[,58]),missing=NA)
watchlist=list(dval=dval)
m2 = xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-58]), label = data.matrix(train[,58]),missing=NA),
param, nrounds = 10000,
watchlist = watchlist,
print_every_n = 10)
})
results[4,ii,4]=t[3]
# Predict
system.time({
dtest = xgb.DMatrix(data.matrix(test[,-58]),missing=NA)
})
t=system.time({
out = predict(m2, dtest)
})
out=as.integer(out>=0.5)
#compute and store the performance metrics
print(results[4,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[4,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[4,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
#GLMNET (elastic networks) # lasso a=1
t=system.time({
fit2 = glmnet(as.matrix(train)[,-58], train$X, family="binomial")
})
results[5,ii,4]=t[3]
mmm=as.matrix(test[,-58])
mmm[which(is.na(mmm))]=0
t=system.time({
out = predict(fit2,mmm , type = "response")[,fit2$dim[2]]
})
results[5,ii,5]=t[3]
out=as.integer(out>=0.5)
print(results[5,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[5,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[5,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
# ridge a=0
t=system.time({
fit2 = glmnet(as.matrix(train)[,-58], train$X, family="binomial",alpha=0)
})
results[6,ii,4]=t[3]
#Predict
mmm=as.matrix(test[,-58])
mmm[which(is.na(mmm))]=0
t=system.time({
out = predict(fit2,mmm , type = "response")[,fit2$dim[2]]
})
results[6,ii,5]=t[3]
out=as.integer(out>=0.5)
#compute and store the performance metrics
print(results[6,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[6,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[6,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[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[,-58]), "test1.hex")
train1[1:5,]
features = names(train1)[-58]
# in order to make the classification prediction
train1$X = as.factor(train1$X)
t=system.time({
rf1 = h2o.randomForest( stopping_metric = "AUC",
training_frame = train1,
validation_frame = valid1,
x=features,
y="X",
model_id = "rf1",
ntrees = 10000,
stopping_rounds = 3,
score_each_iteration = T,
ignore_const_cols = T,
seed = ii)
})
results[7,ii,4]=t[3]
#Predict
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$predict)))
#compute and store the performance metrics
print(results[7,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[7,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[7,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
#h2o deeplearning
t=system.time({
neo.dl = h2o.deeplearning(x = features, y = "X",hidden=c(200,200,200,200,200,200),
distribution = "bernoulli",
training_frame = train1,
validation_frame = valid1,
seed = ii)
})
#Predict
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$predict)))
#compute and store the performance metrics
print(results[8,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[8,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[8,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
#h2o glm
t=system.time({
neo.glm = h2o.glm(x = features, y = "X",
family = "binomial",
training_frame = train1,
validation_frame = valid1,
#lambda = 0,
#alpha = 0,
lambda_search = F,
seed = ii)
})
#Predict
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$predict)))
#compute and store the performance metrics
print(results[9,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[9,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[9,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
#h2o naive bayes
t=system.time({
neo.nb = h2o.naiveBayes(x = features, y = "X",
training_frame = train1,
validation_frame = valid1,
seed = ii)
})
#Predict
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$predict)))
#compute and store the performance metrics
print(results[10,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[10,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[10,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
})), abort = function(){onerr=TRUE;out=NULL})})
print(results[,ii,1])
}
ids=1:100
ress=results[,ids,]
#make the joint summary of the runs, including min, max and medians of the performance metrics
summary.results=array(data = NA,dim = c(15,15))
for(i in 1:1)
{
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)[1:11]=c("GMJMCMC(AIC)","MJMCMC(AIC)","lXGBOOST(logLik)","tXGBOOST(logLik)","LASSO","RIDGE","RFOREST","DEEPNETS","LR","NAIVEBAYESS","KMEANS")
#write the final reults into the files
write.csv(x = cbind(keys(featgmj),values(featgmj)),file = "/mn/sarpanitu/ansatte-u2/aliaksah/Desktop/package/EMJMCMC/examples/neo classification/spamfeatgmj.csv")
write.csv(x = cbind(keys(featrgmj),values(featrgmj)),file = "/mn/sarpanitu/ansatte-u2/aliaksah/Desktop/package/EMJMCMC/examples/neo classification/spamfeatrgmj.csv")
aliaksah/EMJMCMC2016 documentation built on July 27, 2023, 5:48 a.m.
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#load some required libraries
library(RCurl)
library(glmnet)
library(xgboost)
library(h2o)
library(BAS)
library(caret)
#define your working directory, where the data files are stored
workdir=""
#read in the train and test data sets
data = read.table("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/supplementaries/BGNLM/spam/spam.data",col.names=c(paste("x",1:57,sep=""),"X"))
data[,1:57] = scale(data[,1:57])
set.seed(1)
spam.traintest = read.table("https://raw.githubusercontent.com/aliaksah/EMJMCMC2016/master/supplementaries/BGNLM/spam/spam.traintest")#rbinom(n = dim(data)[1],size = 1,prob = 0.01)
train = data[spam.traintest==1,]
test = data[spam.traintest==0,]
data.example = train
#prepare the data structures for the final results
results=array(0,dim = c(11,100,5))
#GMJMCMC
# h2o initialize
h2o.init(nthreads=-1, max_mem_size = "6G")
h2o.removeAll()
for(ii in 1:100)
{
print(paste("iteration ",ii))
capture.output({withRestarts(tryCatch(capture.output({
#here we are no longer running DBRM, since DBRM algorithms are run via other scripts
#for computational efficiency and speed
set.seed(ii)
#xGboost logloss gblinear
t=system.time({
param = list(objective = "binary:logistic",
eval_metric = "logloss",
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)
dval=xgb.DMatrix(data = data.matrix(train[,-58]), label = data.matrix(train[,58]),missing=NA)
watchlist=list(dval=dval)
m2 = xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-58]), label = data.matrix(train[,58]),missing=NA),
param, nrounds = 10000,
watchlist = watchlist,
print_every_n = 10)
})
# Predict
results[3,ii,4]=t[3]
t=system.time({
dtest = xgb.DMatrix(data.matrix(test[,-58]),missing=NA)
})
t=system.time({
out = predict(m2, dtest)
})
results[3,ii,5]=t[3]
out=as.integer(out>=0.5)
#compute and store the performance metrics
print( results[3,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[3,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[3,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
# xgboost logLik gbtree
t=system.time({
param = list(objective = "binary:logistic",
eval_metric = "logloss",
booster = "gbtree",
eta = 0.05,
subsample = 0.86,
colsample_bytree = 0.92,
colsample_bylevel = 0.9,
min_child_weight = 0,
gamma = 0.005,
max_depth = 15)
dval=xgb.DMatrix(data = data.matrix(train[,-58]), label = data.matrix(train[,58]),missing=NA)
watchlist=list(dval=dval)
m2 = xgb.train(data = xgb.DMatrix(data = data.matrix(train[,-58]), label = data.matrix(train[,58]),missing=NA),
param, nrounds = 10000,
watchlist = watchlist,
print_every_n = 10)
})
results[4,ii,4]=t[3]
# Predict
system.time({
dtest = xgb.DMatrix(data.matrix(test[,-58]),missing=NA)
})
t=system.time({
out = predict(m2, dtest)
})
out=as.integer(out>=0.5)
#compute and store the performance metrics
print(results[4,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[4,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[4,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
#GLMNET (elastic networks) # lasso a=1
t=system.time({
fit2 = glmnet(as.matrix(train)[,-58], train$X, family="binomial")
})
results[5,ii,4]=t[3]
mmm=as.matrix(test[,-58])
mmm[which(is.na(mmm))]=0
t=system.time({
out = predict(fit2,mmm , type = "response")[,fit2$dim[2]]
})
results[5,ii,5]=t[3]
out=as.integer(out>=0.5)
print(results[5,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[5,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[5,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
# ridge a=0
t=system.time({
fit2 = glmnet(as.matrix(train)[,-58], train$X, family="binomial",alpha=0)
})
results[6,ii,4]=t[3]
#Predict
mmm=as.matrix(test[,-58])
mmm[which(is.na(mmm))]=0
t=system.time({
out = predict(fit2,mmm , type = "response")[,fit2$dim[2]]
})
results[6,ii,5]=t[3]
out=as.integer(out>=0.5)
#compute and store the performance metrics
print(results[6,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[6,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[6,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[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[,-58]), "test1.hex")
train1[1:5,]
features = names(train1)[-58]
# in order to make the classification prediction
train1$X = as.factor(train1$X)
t=system.time({
rf1 = h2o.randomForest( stopping_metric = "AUC",
training_frame = train1,
validation_frame = valid1,
x=features,
y="X",
model_id = "rf1",
ntrees = 10000,
stopping_rounds = 3,
score_each_iteration = T,
ignore_const_cols = T,
seed = ii)
})
results[7,ii,4]=t[3]
#Predict
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$predict)))
#compute and store the performance metrics
print(results[7,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[7,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[7,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
#h2o deeplearning
t=system.time({
neo.dl = h2o.deeplearning(x = features, y = "X",hidden=c(200,200,200,200,200,200),
distribution = "bernoulli",
training_frame = train1,
validation_frame = valid1,
seed = ii)
})
#Predict
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$predict)))
#compute and store the performance metrics
print(results[8,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[8,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[8,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
#h2o glm
t=system.time({
neo.glm = h2o.glm(x = features, y = "X",
family = "binomial",
training_frame = train1,
validation_frame = valid1,
#lambda = 0,
#alpha = 0,
lambda_search = F,
seed = ii)
})
#Predict
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$predict)))
#compute and store the performance metrics
print(results[9,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[9,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[9,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
#h2o naive bayes
t=system.time({
neo.nb = h2o.naiveBayes(x = features, y = "X",
training_frame = train1,
validation_frame = valid1,
seed = ii)
})
#Predict
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$predict)))
#compute and store the performance metrics
print(results[10,ii,1]=(1-sum(abs(out-test$X[1:length(out)]))/length(out)))
#FNR
ps=which(test$X==1)
results[10,ii,2]=sum(abs(out[ps]-test$X[ps]))/(sum(abs(out[ps]-test$X[ps]))+length(ps))
#FPR
ns=which(test$X==0)
results[10,ii,3]=sum(abs(out[ns]-test$X[ns]))/(sum(abs(out[ns]-test$X[ns]))+length(ns))
})), abort = function(){onerr=TRUE;out=NULL})})
print(results[,ii,1])
}
ids=1:100
ress=results[,ids,]
#make the joint summary of the runs, including min, max and medians of the performance metrics
summary.results=array(data = NA,dim = c(15,15))
for(i in 1:1)
{
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)[1:11]=c("GMJMCMC(AIC)","MJMCMC(AIC)","lXGBOOST(logLik)","tXGBOOST(logLik)","LASSO","RIDGE","RFOREST","DEEPNETS","LR","NAIVEBAYESS","KMEANS")
#write the final reults into the files
write.csv(x = cbind(keys(featgmj),values(featgmj)),file = "/mn/sarpanitu/ansatte-u2/aliaksah/Desktop/package/EMJMCMC/examples/neo classification/spamfeatgmj.csv")
write.csv(x = cbind(keys(featrgmj),values(featrgmj)),file = "/mn/sarpanitu/ansatte-u2/aliaksah/Desktop/package/EMJMCMC/examples/neo classification/spamfeatrgmj.csv")
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