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iline_model.Rmd
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iline_model.Rmd
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---
title: "inhibitory line classification LASSO and RF"
author: "incheol, hyunsu"
date: "July 26, 2018"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Data load
```{r, message=FALSE, warning=FALSE}
setwd("../Data_prep/divided_revised_notmerged_data")
Itrain <- read.csv("Itrain.csv", row.names = "X")
Itrain.l <- read.csv("Itrain_long.csv", row.names = "X")
Itrain.s <- read.csv("Itrain_short.csv", row.names = "X")
Itrain.r <- read.csv("Itrain_ramp.csv", row.names = "X")
Itest <- read.csv("Itest.csv", row.names = "X")
Itest.l <- read.csv("Itest_long.csv", row.names = "X")
Itest.s <- read.csv("Itest_short.csv", row.names = "X")
Itest.r <- read.csv("Itest_ramp.csv", row.names = "X")
```
# set Cross-validation as k-fold
```{r, message=FALSE, warning=FALSE}
require(caret)
fitControl <- trainControl(method = "repeatedcv", number = 10, repeats = 5)
noCV <- trainControl(method = "none")
tuneGrid <- expand.grid(alpha = 1, lambda = seq(0.0001, 1, length = 10))
noGrid <- expand.grid(alpha = 1, lambda = 0.01)
```
# LASSO traning
```{r, message=FALSE, warning=FALSE}
doMC::registerDoMC(cores = 4) # multi core calculation
set.seed(849)
I.reg.full.cv <- caret::train(transgenic_line ~ ., data = Itrain, method = "glmnet", trControl = fitControl, tuneGrid = tuneGrid)
I.reg.full.nocv <-caret::train(transgenic_line ~ ., data = Itrain, method = "glmnet", trControl = noCV, tuneGrid = noGrid)
I.reg.long.cv <- caret::train(transgenic_line ~ ., data = Itrain.l, method = "glmnet", trControl = fitControl, tuneGrid = tuneGrid)
I.reg.long.nocv <-caret::train(transgenic_line ~ ., data = Itrain.l, method = "glmnet", trControl = noCV, tuneGrid = noGrid)
I.reg.short.cv <- caret::train(transgenic_line ~ ., data = Itrain.s, method = "glmnet", trControl = fitControl, tuneGrid = tuneGrid)
I.reg.short.nocv <-caret::train(transgenic_line ~ ., data = Itrain.s, method = "glmnet", trControl = noCV, tuneGrid = noGrid)
I.reg.ramp.cv <- caret::train(transgenic_line ~ ., data = Itrain.r, method = "glmnet", trControl = fitControl, tuneGrid = tuneGrid)
I.reg.ramp.nocv <-caret::train(transgenic_line ~ ., data = Itrain.r, method = "glmnet", trControl = noCV, tuneGrid = noGrid)
```
# Confusion matrix and Prediction accuracy
```{r, message=FALSE, warning=FALSE}
cat("\n\n\n--------- Transgenic_line Inhibitory prediction / Full model / LASSO with CV ----------\n")
a<-predict(I.reg.full.cv, newdata = Itest) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic_line Inhibitory prediction / Full model / LASSO without CV ----------\n")
a<-predict(I.reg.full.nocv, newdata = Itest) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic_line Inhibitory prediction / Long model / LASSO with CV ----------\n")
a<-predict(I.reg.long.cv, newdata = Itest.l) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic_line Inhibitory prediction / Long model / LASSO without CV ----------\n")
a<-predict(I.reg.long.nocv, newdata = Itest.l) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic_line Inhibitory prediction / Short model / LASSO with CV ----------\n")
a<-predict(I.reg.short.cv, newdata = Itest.s) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic_line Inhibitory prediction / Short model / LASSO without CV ----------\n")
a<-predict(I.reg.short.nocv, newdata = Itest.s) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic_line Inhibitory prediction / Ramp model / LASSO with CV ----------\n")
a<-predict(I.reg.ramp.cv, newdata = Itest.r) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic_line Inhibitory prediction / Ramp model / LASSO without CV ----------\n")
a<-predict(I.reg.ramp.nocv, newdata = Itest.r) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
```
# Random Forest training
```{r, message=FALSE, warning=FALSE}
doMC::registerDoMC(cores = 4) # multi core calculation
set.seed(849)
I.rf.full.cv <- caret::train(transgenic_line ~ ., data = Itrain, method = "rf", trControl = fitControl)
I.rf.full.nocv <-caret::train(transgenic_line ~ ., data = Itrain, method = "rf", trControl = noCV)
I.rf.long.cv <- caret::train(transgenic_line ~ ., data = Itrain.l, method = "rf", trControl = fitControl)
I.rf.long.nocv <-caret::train(transgenic_line ~ ., data = Itrain.l, method = "rf", trControl = noCV)
I.rf.short.cv <- caret::train(transgenic_line ~ ., data = Itrain.s, method = "rf", trControl = fitControl)
I.rf.short.nocv <-caret::train(transgenic_line ~ ., data = Itrain.s, method = "rf", trControl = noCV)
I.rf.ramp.cv <- caret::train(transgenic_line ~ ., data = Itrain.r, method = "rf", trControl = fitControl)
I.rf.ramp.nocv <-caret::train(transgenic_line ~ ., data = Itrain.r, method = "rf", trControl = noCV)
```
# Confusion matrix and Prediction accuracy
```{r, message=FALSE, warning=FALSE}
library(magrittr)
cat("\n\n\n--------- Transgenic line Inhibitory prediction / Full model / RANDOM FOREST with CV ----------\n")
a<-predict(I.rf.full.cv, newdata = Itest) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic line Inhibitory prediction / Full model / RANDOM FOREST without CV ----------\n")
a<-predict(I.rf.full.nocv, newdata = Itest) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic line Inhibitory prediction / Long model / RANDOM FOREST with CV ----------\n")
a<-predict(I.rf.long.cv, newdata = Itest.l) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic line Inhibitory prediction / Long model / RANDOM FOREST without CV ----------\n")
a<-predict(I.rf.long.nocv, newdata = Itest.l) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic line Inhibitory prediction / Short model / RANDOM FOREST with CV ----------\n")
a<-predict(I.rf.short.cv, newdata = Itest.s) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic line Inhibitory prediction / Short model / RANDOM FOREST without CV ----------\n")
a<-predict(I.rf.short.nocv, newdata = Itest.s) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic line Inhibitory prediction / Ramp model / RANDOM FOREST with CV ----------\n")
a<-predict(I.rf.ramp.cv, newdata = Itest.r) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
cat("\n\n\n--------- Transgenic line Inhibitory prediction / Ramp model / RANDOM FOREST without CV ----------\n")
a<-predict(I.rf.ramp.nocv, newdata = Itest.r) %>% confusionMatrix(Itest$transgenic_line)
a$table
cat("\n")
a$overall[c(1,3:4)]
```
# Saving models
```{r, message=FALSE, warning=FALSE}
setwd("../lasso_rf")
save(I.reg.full.cv, file = "./R_notmerged_models/I.reg.full.cv.rda")
save(I.reg.full.nocv, file = "./R_notmerged_models/I.reg.full.nocv.rda")
save(I.reg.long.cv, file = "./R_notmerged_models/I.reg.long.cv.rda")
save(I.reg.long.nocv, file = "./R_notmerged_models/I.reg.long.nocv.rda")
save(I.reg.short.cv, file = "./R_notmerged_models/I.reg.short.cv.rda")
save(I.reg.short.nocv, file = "./R_notmerged_models/I.reg.short.nocv.rda")
save(I.reg.ramp.cv, file = "./R_notmerged_models/I.reg.ramp.cv.rda")
save(I.reg.ramp.nocv, file = "./R_notmerged_models/I.reg.ramp.nocv.rda")
save(I.rf.full.cv, file = "./R_notmerged_models/I.rf.full.cv.rda")
save(I.rf.full.nocv, file = "./R_notmerged_models/I.rf.full.nocv.rda")
save(I.rf.long.cv, file = "./R_notmerged_models/I.rf.long.cv.rda")
save(I.rf.long.nocv, file = "./R_notmerged_models/I.rf.long.nocv.rda")
save(I.rf.short.cv, file = "./R_notmerged_models/I.rf.short.cv.rda")
save(I.rf.short.nocv, file = "./R_notmerged_models/I.rf.short.nocv.rda")
save(I.rf.ramp.cv, file = "./R_notmerged_models/I.rf.ramp.cv.rda")
save(I.rf.ramp.nocv, file = "./R_notmerged_models/I.rf.ramp.nocv.rda")
```