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_TEST_Me.R
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_TEST_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 where the models have been trained
#####################################################################
#####################################################################
setwd("~/Documents/asap3_short_answer")
#####################################################################
#####################################################################
# get access to objects on cache
#####################################################################
#####################################################################
library(SOAR)
Objects()
#####################################################################
#####################################################################
# set version and items to predict
#####################################################################
#####################################################################
# training version to be used
Version="V1"
Store(Version)
# item list
items_to_predict=
c(
"3_51802_VS_51802_1","5_50786_VS_50786_1","5_50868_VB_50868_1","5_50932_VS_50932_1",
"5_53299_VB_53299_1","6_50577_VS_50577_1","6_52909_VB_52909_1","7_46115_VS_46115_1",
"7_46121_VS_46121_1","7_46282_VS_46282_1","7_46348_VS_46348_1","7_46450_VS_46450_1",
"7_46597_VS_46597_1","7_46793_VS_46793_1","8_46203_VB_46203_1","8_46517_VB_46517_1",
"8_48558_VS_48558_1","8_48560_VB_48560_1","8_53040_VB_53041_1","9_45535_VS_45535_1",
"9_48247_VB_48247_1","9_51416_VS_51416_1",
"10_46619_VS_46619_1","11_49151_VS_49151_1",
"11_49790_VS_49790_1")
Store(items_to_predict)
item_4valid=gsub("_VS_","_TS_",items_to_predict)
item_4valid=gsub("_VB_","_TB_",item_4valid)
Store(item_4valid)
#####################################################################
#####################################################################
# inform if it is a TRAIN or TEST RUN
#####################################################################
#####################################################################
RUN_TYPE = "TEST"
Store(RUN_TYPE)
print("############################################################")
print(RUN_TYPE)
#####################################################################
for (item in item_4valid) {
item_to_test<-items_to_predict[grep(item,item_4valid)]
Store(item_to_test)
print("############################################################")
print(paste("Item used for training:",item))
print(paste("Item to test:",item_to_test))
print("############################################################")
Store(item)
print("############################################################")
print("# Read data")
#####################################################################
source("_READ_ONE_TEST_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("############################################################")
seed=2013 ; Store(seed)
for (DTM_name in c(paste("_DTM_Words_",nb_word_grams_max,"grams",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_H_",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)
load(paste("Working_files/",Version,"_H_",item,DTM_name,".RData",sep=""))
x=data.frame(bin_mat)
source("_FEATURE_SEL_wRF.R")
print("############################################################")
}
gc()
print("############################################################")
print("# Principal Component Analysis")
print("############################################################")
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,"_H_",item,DTM_name,".RData",sep=""))
source("_PCA.R")
}
gc()
print("############################################################")
print("# Test Trees")
print("############################################################")
DTM_name =paste("_Small2_DTM_Words_",nb_word_grams_max,"grams",sep="")
print(DTM_name)
load(paste("Working_files/",Version,"_H_",item,DTM_name,".RData",sep=""))
load(paste("Working_files/",Version,"_",item,"_Trees_w",DTM_name,".RData",sep=""))
library(gbm)
RF_H<-GBM_H<-0
for (i in 1:length(K)) {
RF_H<-RF_H+predict(RF$fit[[i]],x[[i]])/length(K)
GBM_H<-GBM_H+predict(GBM$fit[[i]],x[[i]],n.trees=GBM$best_ntrees)/length(K)
}
save(RF_H,GBM_H,file=paste("Working_files/",Version,"_H_",item,"_Trees_w",DTM_name,".RData",sep=""))
gc()
print("############################################################")
print('ADD PROXIES')
print("############################################################")
DTM_name =paste("_Small2_DTM_Words_",nb_word_grams_max,"grams",sep="")
load(paste("Working_files/",Version,"_H_",item,DTM_name,".RData",sep=""))
load(paste("Working_files/",Version,"_H_",item,"_PROXIES.RData",sep=""))
DTM_name<-paste(DTM_name,"_PROXIES",sep="")
print(DTM_name)
for (i in 1:length(x)) x[[i]]<-data.frame(x[[i]],PROXIES[,-1])
load(paste("Working_files/",Version,"_",item,"_Trees_w",DTM_name,".RData",sep=""))
RF_H<-GBM_H<-0
for (i in 1:length(K)) {
RF_H<-RF_H+predict(RF$fit[[i]],x[[i]])/length(K)
GBM_H<-GBM_H+predict(GBM$fit[[i]],x[[i]],n.trees=GBM$best_ntrees)/length(K)
}
save(RF_H,GBM_H,file=paste("Working_files/",Version,"_H_",item,"_Trees_w",DTM_name,".RData",sep=""))
gc()
print("############################################################")
print("# Test GLMNET and SVMs")
print("############################################################")
seed=675 ; Store(seed)
for (DTM_name in c(paste("_Small2_DTM_Words_",nb_word_grams_max,"grams",sep=""),
paste("_Small_DTM_Chars_",nb_char_grams_max1,"grams",sep=""),
paste("_PCA_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_max2,"grams",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_H_",item,DTM_name,".RData",sep=""))
load(paste("Working_files/",Version,"_",item,"_NonTrees_w",DTM_name,".RData",sep=""))
library(e1071)
GLMNET_H<-SVM_lin_H<-SVM_rad_H<-0
for (i in 1:length(K)) {
GLMNET_H<-GLMNET_H+predict(GLMNET$fit[[i]],as.matrix(x[[i]]),
type='response', s='lambda.min')[,1]/length(K)
SVM_lin_H<-SVM_lin_H+predict(SVM_lin$fit[[i]],as.matrix(x[[i]]))/length(K)
SVM_rad_H<-SVM_rad_H+predict(SVM_rad$fit[[i]],as.matrix(x[[i]]))/length(K)
}
save(GLMNET_H,SVM_lin_H,SVM_rad_H,
file=paste("Working_files/",Version,"_H_",item,"_NonTrees_w",DTM_name,".RData",sep=""))
}
gc()
print("############################################################")
print("# Group Individual Predictions")
print("############################################################")
Mod2Blend<-data.frame(y=rep(NA,Nrows))
for (DTM_name in c(paste("DTM_Words_",nb_word_grams_max,"grams",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_H_",item,"_GLMNET_4fs_",DTM_name,".RData",sep=""))
Mod2Blend<-data.frame(Mod2Blend,GLMNET_4fs_H)
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,"_H_",item,"_RF_4fs_",DTM_name,".RData",sep=""))
Mod2Blend<-data.frame(Mod2Blend,RF_4fs_H)
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",sep=""),
paste("Small2_DTM_Words_",nb_word_grams_max,"grams_PROXIES",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_H_",item,"_Trees_w_",DTM_name,".RData",sep=""))
Mod2Blend<-data.frame(Mod2Blend,RF_H,GBM_H)
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",sep=""),
paste("Small_DTM_Chars_",nb_char_grams_max1,"grams",sep=""),
paste("PCA_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_max2,"grams",sep=""))) {
print(DTM_name)
load(paste("Working_files/",Version,"_H_",item,"_NonTrees_w_",DTM_name,".RData",sep=""))
Mod2Blend<-cbind(Mod2Blend,
GLMNET_H,
SVM_lin_H,
SVM_rad_H)
names(Mod2Blend)[ncol(Mod2Blend)+(-2:0)]<-
paste(DTM_name,c("GLMNET","SVM_lin","SVM_rad"),sep="_")
}
save(Mod2Blend,file=paste("Working_files/",Version,"_H_",item,"_Mod2Blend.RData",sep=""))
print("############################################################")
}
gc()
print("############################################################")
print("# Predict Blend")
#####################################################################
for (item in item_4valid) {
load(paste("Working_files/",Version,"_",item,"_NNLS.RData",sep=""))
load(paste("Working_files/",Version,"_H_",item,"_Mod2Blend.RData",sep=""))
NNLS_H<-0
for (i in 1:length(NNLS$fit))
NNLS_H<-NNLS_H+as.matrix(Mod2Blend[,-1])%*% NNLS$fit[[i]]/length(NNLS$fit)
print(item)
save(NNLS_H,file=paste("Working_files/",Version,"_H_",item,"_NNLS.RData",sep=""))
}
print("############################################################")
print("# Adjust to optimize Kappa scores")
#####################################################################
for (item in item_4valid) {
load(paste("Working_files/",Version,"_",item,"_Mod2Blend.RData",sep=""))
load(paste("Working_files/",Version,"_",item,"_ADJ1.RData",sep=""))
load(paste("Working_files/",Version,"_H_",item,"_NNLS.RData",sep=""))
ADJ1_H<-0
for (i in 1:length(ADJ1$fit)) {
xx<-ADJ1$fit[[i]]$par
ADJ1_H<-ADJ1_H+(xx[1]+NNLS_H*xx[2])/length(ADJ1$fit)
}
ADJ1_H<-pmax(pmin(ADJ1_H,max(Mod2Blend$y)),min(Mod2Blend$y))
print(item)
save(ADJ1_H,file=paste("Working_files/",Version,"_H_",item,"_ADJ1.RData",sep=""))
}
print("############################################################")
# write csv output
#####################################################################
for (item_to_test in items_to_predict) {
test<-read.csv(paste(wd_path,'/Input/frompems/',item_to_test,'.tsv',sep=""), header=TRUE,
sep = "\t", fileEncoding="windows-1252",quote="")
load(paste("Working_files/",Version,"_H_",item_4valid[grep(item_to_test,items_to_predict)],"_ADJ1.RData",sep=""))
write.csv(data.frame(test,prediction=ADJ1_H),
paste("Output/",Version,"_",item_to_test,".csv",sep=""),row.names=F)
}