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_RUN_Me.R
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_RUN_Me.R
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#####################################################################
#####################################################################
# load packages
#####################################################################
#####################################################################
library(SOAR) # to store data that will be used by all models
library(Matrix) # to handle sparse matrix
library(tau) # for n-grams
library(RTextTools) # to stem words
#####################################################################
#####################################################################
# set and store working directory
#####################################################################
#####################################################################
wd_path<-"~/Documents/asap3_essay"
setwd(wd_path)
Store(wd_path)
#####################################################################
#####################################################################
# set and store parameters to are used for all items
#####################################################################
#####################################################################
# training version
Version="V1"
Store(Version)
# item list
item_list=
c("5_56196_TB_56196_1",
"5_56274_TB_56274_1",
"6_55103_TB_55103_1",
"6_55927_TB_55927_1",
"8_53045_TB_53045_1")
Store(item_list)
# name of response to be modelled
Response_name_A="Final_Score_A"
Response_name_B="Final_Score_B"
Response_name_C="Final_Score_C"
Store(Response_name_A,
Response_name_B,
Response_name_C)
# nb of cores for parallel processing
nb_cores=5
# nb of folds for cross validation
nb_folds_training=5
Store(nb_cores,nb_folds_training)
source("_SETTING.R")
source("_VOCAB.R")
#####################################################################
#####################################################################
# inform if it is a TRAIN or TEST RUN
#####################################################################
#####################################################################
RUN_TYPE = "TRAIN"
Store(RUN_TYPE)
print("############################################################")
print(RUN_TYPE)
#####################################################################
for (item in item_list) {
print("############################################################")
print(paste("Item:",item))
print("############################################################")
Store(item)
print("############################################################")
print("# Read data")
#####################################################################
source("_READ_ONE_TRAIN_FILE.R")
print("############################################################")
print("# Generate Proxies")
print("############################################################")
source("_PROXIES.R")
print("############################################################")
print("# Convert numbers into string")
print("############################################################")
source("_NUMBERS.R")
print("############################################################")
print("# Generate Document Term Matrix")
print("############################################################")
print("# based on word n-grams")
source("_DTM_WORDS.R")
print("############################################################")
print("# based on character n-grams")
nb_char_grams_max=nb_char_grams_max1
source("_DTM_CHARS.R")
nb_char_grams_max=nb_char_grams_max2
source("_DTM_CHARS.R")
gc()
print("############################################################")
print("# Feature selection")
print("############################################################")
for (Response_id in c("A","B","C")) {
print(paste("Response",Response_id))
eval(parse(text=paste("y<-y",Response_id,sep="")))
Store(y, Response_id)
seed=2013 ; Store(seed)
for (DTM_name in c(paste("_DTM_Words_",nb_word_grams_max,"grams",sep=""))) {
print(DTM_name)
seed=2013+seed ; Store(seed)
load(paste("Working_files/",Version,"_",item,DTM_name,".RData",sep=""))
x=data.frame(bin_mat)
source("_FEATURE_SEL_wGLMNET.R")
print("############################################################")
}
for (DTM_name in c(paste("_DTM_Chars_",nb_char_grams_max1,"grams",sep=""),
paste("_DTM_Chars_",nb_char_grams_max2,"grams",sep=""))) {
print(DTM_name)
seed=2013+seed ; Store(seed)
load(paste("Working_files/",Version,"_",item,DTM_name,".RData",sep=""))
x=data.frame(bin_mat)
source("_FEATURE_SEL_wRF.R")
print("############################################################")
}
}
gc()
print("############################################################")
print("# Principal Component Analysis")
print("############################################################")
for (Response_id in c("A","B","C")) {
print(paste("Response",Response_id))
for (DTM_name in c(paste("_Small_DTM_Chars_",nb_char_grams_max1,"grams",sep=""),
paste("_Small_DTM_Chars_",nb_char_grams_max2,"grams",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_",Response_id,
"_",item,DTM_name,".RData",sep=""))
source("_PCA.R")
}
}
gc()
print("############################################################")
print("# Train Trees with Doc Term Matrix and PROXIES")
print("############################################################")
for (Response_id in c("A","B","C")) {
print(paste("Response",Response_id))
eval(parse(text=paste("y<-y",Response_id,sep="")))
Store(y, Response_id)
for (DTM_name in c(paste("_Small2_DTM_Words_",nb_word_grams_max,"grams",sep=""))) {
load(paste("Working_files/",Version,"_",Response_id,
"_",item,DTM_name,".RData",sep=""))
load(paste("Working_files/",Version,"_",item,"_PROXIES.RData",sep=""))
for (i in 1:length(x)) x[[i]]<-data.frame(x[[i]],PROXIES[,-1])
DTM_name<-paste(DTM_name,"_PROXIES",sep="")
print(DTM_name)
GBM_interaction.depth=12
source("_TRAIN_TREES.R")
}
}
gc()
print("############################################################")
print("# Train SVM, GLMNET with Doc Term Matrix and PROXIES")
print("############################################################")
for (Response_id in c("A","B","C")) {
print(paste("Response",Response_id))
eval(parse(text=paste("y<-y",Response_id,sep="")))
Store(y, Response_id)
for (DTM_name in c(paste("_Small_DTM_Chars_",nb_char_grams_max1,"grams",sep=""),
paste("_Small_DTM_Chars_",nb_char_grams_max2,"grams",sep=""),
paste("_PCA_Small_DTM_Chars_",nb_char_grams_max1,"grams",sep=""),
paste("_PCA_Small_DTM_Chars_",nb_char_grams_max2,"grams",sep=""),
paste("_Small2_DTM_Words_",nb_word_grams_max,"grams",sep=""))) {
load(paste("Working_files/",Version,"_",Response_id,
"_",item,DTM_name,".RData",sep=""))
load(paste("Working_files/",Version,"_",item,"_PROXIES.RData",sep=""))
source("CV_RIDIT.R")
PROXIES_RIDIT<-CV_RIDIT(PROXIES,K,5)
save(PROXIES_RIDIT,file=paste("Working_files/",Version,"_",Response_id,
"_",item,"_PROXIES_RIDIT.RData",sep=""))
for (i in 1:length(x)) x[[i]]<-data.frame(x[[i]],PROXIES_RIDIT$x[[i]][,-1])
DTM_name<-paste(DTM_name,"_PROXIES",sep="")
print(DTM_name)
source("_TRAIN_NONTREES.R")
}
}
gc()
print("############################################################")
print("# Group Individual Models")
print("############################################################")
for (Response_id in c("A","B","C")) {
print(paste("Response",Response_id))
eval(parse(text=paste("y<-y",Response_id,sep="")))
Store(y, Response_id)
Mod2Blend=data.frame(y=y)
for (DTM_name in c(paste("DTM_Words_",nb_word_grams_max,"grams",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_",Response_id,
"_",item,"_GLMNET_4fs_",DTM_name,".RData",sep=""))
Mod2Blend<-data.frame(Mod2Blend,GLMNET_4fs$yhatV)
names(Mod2Blend)[ncol(Mod2Blend)]<-paste(DTM_name,c("GLMNET_4fs"),sep="_")
}
for (DTM_name in c(paste("DTM_Chars_",nb_char_grams_max1,"grams",sep=""),
paste("DTM_Chars_",nb_char_grams_max2,"grams",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_",Response_id,
"_",item,"_RF_4fs_",DTM_name,".RData",sep=""))
Mod2Blend<-data.frame(Mod2Blend,RF_4fs$yhatV)
names(Mod2Blend)[ncol(Mod2Blend)]<-paste(DTM_name,c("RF_4fs"),sep="_")
}
for (DTM_name in c(paste("Small2_DTM_Words_",nb_word_grams_max,"grams_PROXIES",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_",Response_id,
"_",item,"_Trees_w_",DTM_name,".RData",sep=""))
Mod2Blend<-data.frame(Mod2Blend,RF$yhatV,GBM$yhatV)
names(Mod2Blend)[ncol(Mod2Blend)+(-1:0)]<-paste(DTM_name,c("RF","GBM"),sep="_")
}
for (DTM_name in c(paste("Small2_DTM_Words_",nb_word_grams_max,"grams_PROXIES",sep=""),
paste("Small_DTM_Chars_",nb_char_grams_max1,"grams_PROXIES",sep=""),
paste("PCA_Small_DTM_Chars_",nb_char_grams_max1,"grams_PROXIES",sep=""),
paste("Small_DTM_Chars_",nb_char_grams_max2,"grams_PROXIES",sep=""),
paste("PCA_Small_DTM_Chars_",nb_char_grams_max2,"grams_PROXIES",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_",Response_id,
"_",item,"_NonTrees_w_",DTM_name,".RData",sep=""))
Mod2Blend<-cbind(Mod2Blend,
GLMNET$yhatV,
SVM_lin$yhatV,
SVM_rad$yhatV)
names(Mod2Blend)[ncol(Mod2Blend)+(-2:0)]<-
paste(DTM_name,c("GLMNET","SVM_lin","SVM_rad"),sep="_")
}
save(Mod2Blend,file=paste("Working_files/",Version,"_",Response_id,
"_",item,"_Mod2Blend.RData",sep=""))
}
print("############################################################")
}
gc()
print("############################################################")
print("# Blend")
#####################################################################
source("CV_NNLS.R")
source("_KFolds.R")
for (item in item_list) {
for (Response_id in c("A","B","C")) {
print(paste("Response",Response_id))
load(paste("Working_files/",Version,"_",Response_id,
"_",item,"_Mod2Blend.RData",sep=""))
Kb<-Kfolds(896,nb_folds_training,nrow(Mod2Blend))
NNLS<-CV_NNLS(x=Mod2Blend[,-1],y=Mod2Blend$y,
K=Kb,nb_cores=nb_cores,seed=9867)
print(item)
save(NNLS,Kb,file=paste("Working_files/",Version,"_",Response_id,
"_",item,"_NNLS.RData",sep=""))
}
}
gc()
print("############################################################")
print("# Adjust to optimize Kappa scores")
#####################################################################
source("CV_ADJ.R")
source("_KFolds.R")
for (item in item_list) {
for (Response_id in c("A","B","C")) {
print(paste("Response",Response_id))
load(paste("Working_files/",Version,"_",Response_id,
"_",item,"_Mod2Blend.RData",sep=""))
load(paste("Working_files/",Version,"_",Response_id,
"_",item,"_NNLS.RData",sep=""))
Kc<-Kfolds(896,nb_folds_training,length(NNLS$yhatV))
ADJ1<-CV_ADJ(x=NNLS$yhatV,y=Mod2Blend$y,K=Kc,d=1,nb_cores=nb_cores,seed=seed)
save(ADJ1,Kc,file=paste("Working_files/",Version,"_",Response_id,
"_",item,"_ADJ1.RData",sep=""))
print(item)
}
}
for (item in item_list) {
for (Response_id in c("A","B","C")) {
print(paste("Response",Response_id))
load(paste("Working_files/",Version,"_",Response_id,
"_",item,"_Mod2Blend.RData",sep=""))
load(paste("Working_files/",Version,"_",Response_id,
"_",item,"_NNLS.RData",sep=""))
Kc<-Kfolds(896,nb_folds_training,length(NNLS$yhatV))
ADJ2<-CV_ADJ(x=NNLS$yhatV,y=Mod2Blend$y,K=Kc,d=2,nb_cores=nb_cores,seed=seed)
save(ADJ2,Kc,file=paste("Working_files/",Version,"_",Response_id,
"_",item,"_ADJ2.RData",sep=""))
print(item)
}
}