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talkingbattle6.R
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talkingbattle6.R
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train.data <- fread("gender_age_train.csv",colClasses = c("character","factor","integer","factor"))
setkey(train.data,"device_id")
test.data <- fread("gender_age_test.csv",colClasses = c("character"))
setkey(test.data,"device_id")
test.data[,':='(gender=NA,age=NA,group=NA)]
total.data <- rbind(train.data,test.data)
setkey(total.data,device_id)
train.devices <- unique(train.data$device_id)
test.devices <- unique(test.data$device_id)
rm(train.data,test.data)
gc()
brand.info <- fread("phone_brand_device_model.csv",colClasses = c("character","character","character"),encoding = "UTF-8")
brand.info <- unique(brand.info)
setkey(brand.info,"device_id")
brand.info[,device_model := paste0(phone_brand," ",device_model)]
brand.info <- unique(brand.info)
total.info <- merge(total.data,brand.info,all.x = TRUE)
rm(total.data)
gc()
# mygoogleapikey <- "AIzaSyBAFhy9_3dgJHmnDPFhDD2BQiKMDx8c0Hg"
# mapurl <- "https://maps.googleapis.com/maps/api/geocode/json?"
#
# #####################################
# ### Trial###################
# #####################################
# latlng <- c(35.19,118.16)
# newurl <- paste0(mapurl,"latlng=",paste0(latlng,collapse = ","),"&sensor=true","&key=",mygoogleapikey)
# connection <- url(newurl)
# data <- RJSONIO::fromJSON(paste(readLines(connection), collapse=""))
# close(connection)
# data.json <- unlist(data)
# if(data.json["status"]=="OK"){
# data.address <- data.json["results.formatted_address"]
# data.address <- strsplit(data.address,",")[[1]]
# data.address <- stringr::str_trim(data.address)
# data.country <- data.address[length(data.address)]
# data.region <- data.address[length(data.address)-1]
# }
# ###############################
events.info <- fread("events.csv",colClasses = c("character","character","character","numeric","numeric"))
events.info[,timestamp:=parse_datetime(timestamp,"%Y-%m-%d %H:%M:%S")]
events.info[,weekhour:=(wday(timestamp)-1)*24+hour(timestamp)+(minute(timestamp)/60)+(second(timestamp)/3600)]
events.info[,':='(weekday=wday(timestamp,label = TRUE),timeday=hour(timestamp))]
events.info[,timestamp:=NULL]
events.info[,partofday:= as.character(cut(timeday,breaks=c(0,5,12,17,21,24),labels = c("Night","Morning","Afternoon","Evening","Night"),right = FALSE))]
# ###########################################
# ######## Lat Long experiment ###############
# ########################################
#
# mygoogleapikey <- "AIzaSyBAFhy9_3dgJHmnDPFhDD2BQiKMDx8c0Hg"
# mapurl <- "https://maps.googleapis.com/maps/api/geocode/json?"
# getRegionCountry <- function(lat,long){
# makeurl <- paste0(mapurl,"latlng=",lat,",",long,"&sensor=true","&key=",mygoogleapikey)
# connection <- url(makeurl)
# data <- fromJSON(paste(readLines(connection),collapse = ""))
# close(connection)
# data.json <- unlist(data)
# if(data.json["status"]=="OK"){
# data.address <- data.json["results.formatted_address"]
# data.address <- strsplit(data.address,",")[[1]]
# data.address <- str_trim(data.address)
# add.length <- length(data.address)
# if(add.length>4){
# data.country <- data.address[length(data.address)-1]
# data.region <- data.address[length(data.address)-2]
# } else {
# data.country <- data.address[length(data.address)]
# data.region <- data.address[length(data.address)-1]
# }
# return(paste0(data.region,",",data.country))
# } else{
# return("NA")
# }
# }
#
# latlongs <- unique(events.info[,.(latitude,longitude)])
# latlongs[,regionCountry:=getRegionCountry(latitude,longitude),by=1:nrow(latlongs)]
# save(latlongs,file="latlongs.rda")
# #############################################################################
setkeyv(events.info,c("event_id","device_id"))
# China.long <- map_data("world","China")[,"long"]
# China.lat <- map_data("world","China")[,"lat"]
# # SDMTools::pnt.in.poly(events.info[device_id=="4505543221133337925",.(longitude,latitude)],map_data("world","China")[,c("long","lat")])
# events.info[,inChina:=point.in.polygon(longitude,latitude,China.long,China.lat)]
app.events.info <- fread("app_events.csv",colClasses = c("character","character","integer","integer"))
setkeyv(app.events.info,c("event_id","app_id"))
app.labels.info <- fread("app_labels.csv",colClasses = c("character","character"))
app.labels.info <- unique(app.labels.info)
setkeyv(app.labels.info,c("app_id","label_id"))
label.categories.info <- fread("label_categories.csv",colClasses = c("character","character"))
setkey(label.categories.info,"label_id")
app.labels.categories <- merge(app.labels.info,label.categories.info,by="label_id",all.x=TRUE,allow.cartesian=TRUE)
app.labels.categories[,label_id:=NULL]
app.labels.categories <- app.labels.categories[,.(categories=paste0(category,collapse = ",")),by=app_id]
setkey(app.labels.categories,"app_id")
rm(app.labels.info,label.categories.info)
gc()
app.events.info[,categories:=app.labels.categories[app_id,categories]]
events.apps.info <- merge(events.info,app.events.info,by="event_id",all.x=TRUE,allow.cartesian = TRUE)
# events.apps.info[,':='(timestamp=NULL,longitude=NULL,latitude=NULL)]
rm(app.events.info,events.info)
gc()
devices.with.app.info <- events.apps.info[,.(length=.N,NAs=sum(is.na(app_id))),by=.(device_id,app_id)][length>1 & NAs>0,device_id]
# events.apps.info <- events.apps.info[!(device_id%in%devices.with.app.info&is.na(app_id))]
gc()
devices.with.noapp.info <- events.apps.info[is.na(app_id),device_id]
devices.with.noapp.info <- setdiff(devices.with.noapp.info,devices.with.app.info)
# events.apps.inChina <- events.apps.info[inChina>0]
# events.apps.outChina <- events.apps.info[inChina==0]
# rm(events.apps.info)
# gc()
# events.apps.summary.inChina <- events.apps.inChina[,.(Nactivity=sum(is_active,na.rm=TRUE),Pactivity=mean(is_active,na.rm=TRUE)),by=.(device_id,app_id,categories)]
# events.apps.summary.outChina <- events.apps.outChina[,.(Nactivity=sum(is_active,na.rm=TRUE),Pactivity=mean(is_active,na.rm=TRUE)),by=.(device_id,app_id,categories)]
# rm(devices.with.app.info,events.apps.inChina,events.apps.outChina)
# gc()
# final.merge.inChina <- merge(events.apps.inChina,total.info,by="device_id",all.y=TRUE)
# setkeyv(events.apps.inChina,c("device_id","event_id"))
# setkeyv(events.apps.outChina,c("device_id","event_id"))
# gc()
# events.apps.inChina[,':='(gender=total.info[device_id,gender],age=total.info[device_id,age],group=total.info[device_id,group],phone_brand=total.info[device_id,phone_brand],device_model=total.info[device_id,device_model])]
# setkey(events.apps.info,device_id)
# final.merge <- merge(events.apps.info,total.info,by="device_id",all.y=TRUE)
# rm(events.apps.inChina)
# gc()
# save(final.merge.inChina,file="BigMerge1.rda")
# rm(final.merge.inChina)
# gc()
# final.merge.outChina <- merge(events.apps.outChina,total.info,by="device_id",all.y=TRUE)
# setkeyv(final.merge.inChina,c("device_id","app_id"))
# setkeyv(final.merge.outChina,c("device_id","app_id"))
# rm(events.apps.summary.inChina,events.apps.summary.outChina)
# gc()
# final.merge.inChina[!is.na(app_id),app_id:=paste0("App ID (China):",app_id)]
# final.merge.outChina[!is.na(app_id),app_id:=paste0("App ID:",app_id)]
# final.merge.inChina[,':='(phone_brand=paste0("Brand (China):",phone_brand),device_model=paste0("Model:",device_model))]
# final.merge.outChina[,':='(phone_brand=paste0("Brand:",phone_brand),device_model=paste0("Model:",device_model))]
#
# device.categories <- events.apps.info[!is.na(app_id),.(categories=paste0(unique(categories),collapse=",")),by=device_id]
# categories <- strsplit(device.categories[,categories],",")
#
# device.categories <- data.table("device_id"=rep(device.categories[,device_id],unlist(lapply(categories,length))),"categories"=unlist(categories))
#
# device.apps <- events.apps.info[,.(app_id=paste0(unique(app_id),collapse=",")),by=device_id]
# app_ids <- strsplit(device.apps[,app_id],",")
# device.apps <- data.table("device_id"=rep(device.apps[,device_id],unlist(lapply(app_ids,length))),"app_id"=unlist(app_ids))
#
# id_devices <- match(final.merge.inChina[,device_id],unique_devices)
# id_devices.outChina <- match(final.merge.outChina[,device_id],unique_devices)
# id_mapps.inChina <- match(final.merge.inChina[,app_id],unique_mapps)
# id_mapps.outChina <- match(final.merge.outChina[,app_id],unique_mapps)
# id_apps.inChina <- match(final.merge.inChina[,app_id],unique_apps)
# id_apps.outChina <- match(final.merge.outChina[,app_id],unique_apps)
# id_brands.inChina <- match(final.merge.inChina[,phone_brand],unique_brands)
# id_brands.outChina <- match(final.merge.outChina[,phone_brand],unique_brands)
# id_models.inChina <- match(final.merge.inChina[,device_model],unique_models)
# id_models.outChina <- match(final.merge.outChina[,device_model],unique_models)
# id_categories.inChina <- match(device.categories.inChina[,categories],unique_categories)
# id_categories.outChina <- match(device.categories.outChina[,categories],unique_categories)
#
# id_devices_apps.inChina <- cbind(id_devices.inChina,id_apps.inChina) %>% unique()
# id_devices_apps.inChina <- id_devices_apps.inChina[complete.cases(id_devices_apps.inChina),]
# id_devices_mapps.inChina <- cbind(id_devices.inChina,id_mapps.inChina) %>% unique()
# id_devices_brands.inChina <- cbind(id_devices.inChina,id_brands.inChina) %>% unique()
# id_devices_models.inChina <- cbind(id_devices.inChina,id_models.inChina) %>% unique()
#
#
# id_devices_apps.outChina <- cbind(id_devices.outChina,id_apps.outChina) %>% unique()
# id_devices_apps.outChina <- id_devices_apps.outChina[complete.cases(id_devices_apps.outChina),]
# id_devices_mapps.outChina <- cbind(id_devices.outChina,id_mapps.outChina) %>% unique()
# id_devices_brands.outChina <- cbind(id_devices.outChina,id_brands.outChina) %>% unique()
# id_devices_models.outChina <- cbind(id_devices.outChina,id_models.outChina) %>% unique()
#
# id_devices.c.inChina <- match(device.categories.inChina[,device_id],unique_devices)
# id_devices.c.outChina <- match(device.categories.outChina[,device_id],unique_devices)
#
# id_devices_categories.inChina <- cbind(id_devices.c.inChina,id_categories.inChina)
# id_devices_categories.outChina <- cbind(id_devices.c.outChina,id_categories.outChina)
setkey(events.apps.info,device_id)
events.apps.info <- events.apps.info[total.info[,device_id]]
setkey(events.apps.info,device_id)
gc()
events.apps.info[,missingEvents:=ifelse(is.na(event_id),1,0)]
events.apps.summary <- events.apps.info[,.(Nactivity=sum(is_active,na.rm = TRUE),Pactivity=mean(is_active,na.rm=TRUE),missingEvents=max(missingEvents)),by=.(device_id,weekday,partofday,app_id,categories)]
setkey(events.apps.summary,device_id)
rm(events.apps.info)
gc()
final.merge <- events.apps.summary[,':='(phone_brand=total.info[device_id,phone_brand],device_model=total.info[device_id,device_model],group=total.info[device_id,group])]
setkey(final.merge,device_id)
rm(events.apps.summary)
gc()
final.merge[,week_day:=paste0(as.character(weekday),":",as.character(partofday))]
device.categories <- final.merge[!is.na(app_id),.(categories=paste0(unique(categories),collapse=",")),by=.(device_id)]
categories <- strsplit(device.categories[,categories],",")
device.categories <- data.table("device_id"=rep(device.categories[,device_id],unlist(lapply(categories,length))),"categories"=unlist(categories))
device.categories[,Ncategories:=.N,by=.(device_id,categories)]
device.categories <- unique(device.categories)
device.categories[,Pcategories:=Ncategories/sum(Ncategories),by=device_id]
device.week.day.activities <- final.merge[!is.na(app_id),.(Nactivity=sum(Nactivity,na.rm=TRUE),Pactivity=mean(Pactivity,na.rm=TRUE)),by=.(device_id,week_day)]
device.events <- final.merge[,.(device_id,missingEvents)] %>% unique()
unique_devices <- final.merge[,device_id] %>% unique()
unique_devices_with_events <- final.merge[missingEvents==0,device_id] %>% unique()
unique_mapps <- final.merge[missingEvents==0,app_id] %>% unique()
unique_apps <- final.merge[!is.na(app_id),app_id] %>% unique()
unique_brands <- final.merge[,phone_brand] %>% unique()
unique_models <- final.merge[,device_model] %>% unique()
unique_weekday <- final.merge[missingEvents==0,weekday]%>%unique()
unique_day <- final.merge[missingEvents==0,partofday]%>%unique()
unique_week_day <- final.merge[missingEvents==0,week_day] %>% unique()
unique_categories <- device.categories[,categories]%>% unique()
gc()
id_devices_with_events <- match(final.merge[,device_id],unique_devices_with_events)
id_devices <- match(final.merge[,device_id],unique_devices)
id_mapps <- match(final.merge[,app_id],unique_mapps)
id_apps <- match(final.merge[,app_id],unique_apps)
id_brands <- match(final.merge[,phone_brand],unique_brands)
id_models <- match(final.merge[,device_model],unique_models)
id_weekdays <- match(final.merge[,weekday],unique_weekday)
id_days <- match(final.merge[,partofday],unique_day)
id_week_days <- match(final.merge[,week_day],unique_week_day)
id_categories <- match(device.categories[,categories],unique_categories)
id_devices.c <- match(device.categories[,device_id],unique_devices)
id_devices.w <- match(device.week.day.activities[,device_id],unique_devices)
id_week_days.w <- match(device.week.day.activities[,week_day],unique_week_day)
gc()
id_devices_events_apps <- matrix(c(id_devices,id_apps),ncol=2) %>% unique()
id_devices_events_apps <- id_devices_events_apps[complete.cases(id_devices_events_apps),]
id_devices_events_mapps <- matrix(c(id_devices,id_mapps),ncol=2) %>% unique()
id_devices_events_mapps <- id_devices_events_mapps[complete.cases(id_devices_events_mapps),]
id_devices_brands <- cbind(id_devices,id_brands) %>% unique()
id_devices_brands <- id_devices_brands[complete.cases(id_devices_brands),]
id_devices_models <- cbind(id_devices,id_models) %>% unique()
id_devices_models <- id_devices_models[complete.cases(id_devices_models),]
id_devices_weekdays <- matrix(c(id_devices,id_weekdays),ncol=2) %>% unique()
id_devices_weekdays <- id_devices_weekdays[complete.cases(id_devices_weekdays),]
id_devices_days <- matrix(c(id_devices,id_days),ncol=2) %>% unique()
id_devices_days <- id_devices_days[complete.cases(id_devices_days),]
id_devices_week_days <- matrix(c(id_devices,id_week_days),ncol=2) %>% unique()
id_devices_week_days <- id_devices_week_days[complete.cases(id_devices_week_days),]
id_devices_categories <- matrix(c(id_devices.c,id_categories),ncol=2) %>% unique()
id_devices_categories <- id_devices_categories[complete.cases(id_devices_categories),]
id_devices_week_days_activities <- matrix(c(id_devices.w,id_week_days.w),ncol=2) %>% unique()
id_devices_week_days_activities <- id_devices_week_days_activities[complete.cases(id_devices_week_days_activities),]
prev_na_action <- options("na.action")
options(na.action = "na.pass")
device_app.matrix <- Matrix(0,nrow=length(unique_devices),ncol=length(unique_mapps),dimnames=list(unique_devices,unique_mapps),sparse = TRUE)
device_app.matrix[id_devices_events_mapps] <- 1
device_brand.matrix <- Matrix(0,nrow=length(unique_devices),ncol=length(unique_brands),dimnames=list(unique_devices,unique_brands),sparse = TRUE)
device_brand.matrix[id_devices_brands] <- 1
device_models.matrix <- Matrix(0,nrow=length(unique_devices),ncol=length(unique_models),dimnames=list(unique_devices,unique_models),sparse = TRUE)
device_models.matrix[id_devices_models] <- 1
device_weekday.matrix <- Matrix(0,nrow=length(unique_devices),ncol=length(unique_weekday),dimnames=list(unique_devices,unique_weekday%>%as.character()),sparse = TRUE)
device_weekday.matrix[id_devices_weekdays] <- 1
device_day.matrix <- Matrix(0,nrow=length(unique_devices),ncol=length(unique_day),dimnames=list(unique_devices,unique_day),sparse = TRUE)
device_day.matrix[id_devices_days] <- 1
device_week_day.matrix <- Matrix(0,nrow=length(unique_devices),ncol=length(unique_week_day),dimnames=list(unique_devices,unique_week_day),sparse = TRUE)
device_week_day.matrix[id_devices_week_days] <- 1
eps <- 1
device_week_day_activity.matrix <- Matrix(0,nrow=length(unique_devices),ncol=length(unique_week_day),dimnames=list(unique_devices,unique_week_day),sparse = TRUE)
device_week_day_activity.matrix[id_devices_week_days_activities] <- sqrt(device.week.day.activities[,Nactivity])
device_categories.matrix <- Matrix(0,nrow=length(unique_devices),ncol=length(unique_categories),dimnames=list(unique_devices,unique_categories),sparse = TRUE)
device_categories.matrix[id_devices_categories] <- 1
device_categories_size.matrix <- Matrix(0,nrow=length(unique_devices),ncol=length(unique_categories),dimnames=list(unique_devices,unique_categories),sparse = TRUE)
device_categories_size.matrix[id_devices_categories] <- log(device.categories[,Ncategories])
device_events <- sparseVector(x=1,i=device.events[unique_devices,.I[missingEvents==1]],length=length(unique_devices))
device_events.matrix <- Matrix(0,nrow=length(unique_devices),ncol=1,dimnames = list(unique_devices,"missingEvents"),sparse = TRUE)
device_events.matrix[cbind(device.events[unique_devices,.I[missingEvents==1]],1)] <- 1
# for(i in seq_along(events.apps.info)){
# set(events.apps.info,i=which(is.na(events.apps.info[,i,with=FALSE])),j=i,value = 0)
# }
# for(name in names(total.info)){
# # if(!grepl("device_id",name)){
# if(name %in% c("phone_brand","device_model","group")){
# expr <- parse(text = paste0(name,":=total.info[device_id,\"",name,"\",with=FALSE]"))
# events.apps.info[,eval(expr)]
# }
# gc()
# }
#
#
# for(i in seq_along(events.apps.info)){
# if(class(events.apps.info[[i]])=="integer"){
# print("Changing Integer")
# set(events.apps.info,i=which(is.na(events.apps.info[[i]])),j=i,value = 0L)
# } else if(class(events.apps.info[[i]])=="numeric"){
# print("Changing Numeric")
# set(events.apps.info,i=which(is.na(events.apps.info[[i]])),j=i,value = 0.00)
# } else if(class(events.apps.info[[i]])=="character"){
# print("Character")
# }
# }
# events.apps.info[,':='(event_id=NULL,longitude=NULL,latitude=NULL,categories=NULL)]
# events.apps.info[,weekhour:=is_active*weekhour]
# setkey(events.apps.info,"device_id")
# size <- 2^16
#
# gc()
#
X_full <- cbind(device_brand.matrix,device_models.matrix,device_app.matrix,device_categories_size.matrix,device_categories.matrix,device_weekday.matrix,device_day.matrix,device_week_day.matrix,device_week_day_activity.matrix)
# X_full <-cbind(device_brand.matrix,device_models.matrix,device_app.matrix,device_categories.matrix,device_categories_size.matrix)
Y_full <- total.info[rownames(X_full),group]
Y_full <- factor(Y_full)
X_train <- X_full[!is.na(Y_full),]
X_test <- X_full[is.na(Y_full),]
Y_train <- Y_full[!is.na(Y_full)]
Y_test <- Y_full[is.na(Y_full)]
#
ftrain <- xgb.DMatrix(X_train,label=as.integer(Y_train)-1,missing=NA)
# fit.glmnet <- glmnet(x=X_train,y=Y_train,family="multinomial",alpha=0.5,intercept = FALSE)
fit.glmnet.cv <- cv.glmnet(X_train,Y_train,alpha=0.5,nfolds = 5,family="multinomial",intercept=FALSE)
# depth <- 6
# shrk <- 0.025
# ntree <- 500
#
#
# param <- list(booster="gblinear",
# num_class=length(unique(Y_train)),
# objective="multi:softprob",
# eval_metric="mlogloss",
# eta=shrk,
# max.depth=depth,
# lambda=5,
# alpha=3,
# subsample=0.5,
# colsample_bytree=0.5,
# num_parallel_tree=2,
# nthread=2)
#
#
# watchlist <- list(train=ftrain)
#
# set.seed(1812)
#
# fit_cv <- xgb.cv(params=param,
# data=ftrain,
# nrounds=ntree,
# watchlist=watchlist,
# nfold=5,
# early.stop.round=10,
# verbose=1)
# #
#
# ntree <- 106
# fit.xgb <- xgb.train(params=param,
# data=ftrain,
# nrounds=ntree,
# watchlist=watchlist,
# verbose=1)
#
# ftest <- xgb.DMatrix(X_test,label=as.integer(Y_test),missing=NA)
pred <- predict(fit.glmnet,X_test,type="response",lambda=c(fit.glmnet.cv$lambda.min,fit.glmnet.cv$lambda.1se))
pred <- t(matrix(pred,nrow=length(levels(Y_full))))
colnames(pred) <- levels(Y_full)
first_submit <- data.frame(device_id=rownames(pred),pred[,,1])
colnames(first_submit) <- c("device_id",levels(Y_full))
write.csv(first_submit,file="submit1.0.csv",row.names=F,quote=F)
search.grid <- expand.grid(
max_depth = 6,
eta = c(0.01,0.025),
nrounds = 1000,
lambda = c(4,5,6),
alpha=c(1,2,3)
)
# param <- list(booster="gblinear",
# num_class=length(unique(Y_train)),
# objective="multi:softprob",
# eval_metric="mlogloss",
# subsample=0.5,
# colsample_bytree=0.5,
# num_parallel_tree=3)
#
# xgbLinearCtrl <- trainControl(method="repeatedcv",repeats=5,number=5,summaryFunction = mnLogLoss)
# xgbLinearModel <- train(x=X_train,y=as.integer(Y_train)-1,
# method="xgboost",
# maximize = FALSE,
# trControl = xgbLinearCtrl,
# tuneGrid = search.grid,
# params=param
# )
# fit_cv <- xgb.cv(params=param, data=ftrain, nrounds=100000, watchlist=watchlist, nfold=5, early.stop.round=3, verbose=1)
get_cv_mlogloss <- function(x){
start.time <- Sys.time()
param <- list(booster="gblinear",
num_class=length(unique(Y_train)),
objective="multi:softprob",
eval_metric="mlogloss",
eta=search.grid[x,"eta"],
max.depth=search.grid[x,"max_depth"],
lambda=search.grid[x,"lambda"],
alpha=search.grid[x,"alpha"],
subsample=0.5,
colsample_bytree=0.5,
num_parallel_tree=1,
num_thread=1,
nthread=1)
print(start.time)
print(x)
print(search.grid[x,])
fit_cv <- xgb.cv(params=param, data=ftrain, nrounds=search.grid[x,"nrounds"], watchlist=watchlist, nfold=5, early.stop.round=25, verbose=1,print.every.n = 5)
min_tree <- which(fit_cv$test.mlogloss.mean==min(fit_cv$test.mlogloss.mean),arr.ind = TRUE)
end.time <- Sys.time()
print(end.time)
print(end.time-start.time)
return(cbind(index=x,min_tree,search.grid[x,"max_depth"],search.grid[x,"eta"],search.grid[x,"lambda"],search.grid[x,"alpha"],fit_cv[min_tree,]))
}
tune_parameters_mlogloss <- do.call(rbind.data.frame,lapply(1:nrow(search.grid),get_cv_mlogloss))
tune_parameters_mlogloss[,index:=1]
tune_parameters_mlogloss[,index2:=cumsum(index),by=test.mlogloss.mean]
tune_parameters_mlogloss[,index3:=1]
tune_parameters_mlogloss[index2>1,index3:=0]
tune_parameters_mlogloss[,index4:=cumsum(index3)]
print(search.grid[tune_parameters_mlogloss[order(test.mlogloss.mean),index4],],topn=100)
tuned_parameters <- tune_parameters_mlogloss[order(test.mlogloss.mean)]
save(tuned_parameters,file="thirdTuning.rda")
#missing events didn't help