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Tuning.R
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#Basic stuff
{##Absolutely necessary to run initialization BEFORE this file
##Import data
data <- read.csv("LIDC dataset with full annotations.csv",header=TRUE)
img_fs <- data[,c(5:18, 43:69)]
img_fs <- data.frame(img_fs, Avg.Gabor(data))
#Df for results
col <- c("Mode 1", "Mode 2", "Mode 3", "Max Mode", "Set",
"I1 Label", "I1 Pred", "I1 Label Added", "I2 Label",
"I2 Pred","I2 Label Added", "I3 Label", "I3 Pred",
"I3 Label Added", "I4 Label", "I4 Pred")
results <- data.frame(data.frame(matrix(vector(), 810, 16, dimnames=list(c(), col))))
## Label tracker
label.tracker <- rep(1,nrow(data))
labelsum <- list()
#Null objects
train = NULL
test = NULL
valid = NULL
tables <- vector(mode="list",length=20)}
#Tuned controls per iteration
ics = rbind(rpart.control(minsplit = 250, minbucket= round(250/4), cp = 0.01),
rpart.control(minsplit = 150, minbucket= round(150/2), cp = 0.01),
rpart.control(minsplit = 250, minbucket= round(250/6), cp = 0.01),
rpart.control(minsplit = 250, minbucket= round(250/6), cp = 0.01))
for (t in 1:20){
set.seed(t)
##Process labels
#currently iterative labeling for both trail and test
labels <- data[,70:73]
#shuffles labels
labels <- t(apply(labels,1,sample))
#takes the mode for each iteration
labels <- cbind(labels[,1],apply(labels[,1:2],1,mode),
apply(labels[,1:3],1,mode),apply(labels,1,mode))
labels <- apply(labels,c(1,2),rescale)
results[1:4] <- labels
#Separate training, testing and valid
index <- bal_strat(labels)
#Get image features
train$img <- as.matrix(img_fs[index$train,])
test$img <- as.matrix(img_fs[index$test,])
valid$img <- as.matrix(img_fs[index$valid,])
table <- data.frame(data.frame(matrix(vector(), 50, 7,
dimnames=list(c(), c("np","nc","tr","teI",
"diff", "trM","teM")))))
#Controls
{ms = 20
table [1:3,1] = ms
c1 <- rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01)
c2 <- rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01)
c3 <- rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01)
ms = 30
table [4:6,1] = ms
c4 <- rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01)
c5 <- rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01)
c6 <- rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01)
ms = 40
table [7:10,1] = ms
c7 <- rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01)
c8 <- rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01)
c9 <- rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01)
c10 <- rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01)
ms = 50
table [11:14,1] = ms
c11 <- rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01)
c12 <- rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01)
c13 <- rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01)
c14 <- rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01)
ms = 60
table [15:18,1] = ms
c15 <- rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01)
c16 <- rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01)
c17 <- rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01)
c18 <- rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01)
ms = 70
table [19:22,1] = ms
c19 <- rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01)
c20 <- rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01)
c21 <- rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01)
c22 <- rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01)
ms = 100
table [23:26,1] = ms
c23 <- rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01)
c24 <- rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01)
c25 <- rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01)
c26 <- rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01)
ms = 150
table [27:30,1] = ms
c27 <- rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01)
c28 <- rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01)
c29 <- rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01)
c30 <- rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01)
ms = 200
table [31:34,1] = ms
c200 = rbind (rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01))
ms = 250
table [35:38,1] = ms
c250 = rbind (rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01))
ms = 300
table [39:42,1] = ms
c300 = rbind (rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01))
ms = 350
table [43:46,1] = ms
c350 = rbind (rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01))
ms = 400
table [47:50,1] = ms
c400 = rbind (rpart.control(minsplit = ms, minbucket= round(ms/8), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/6), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/4), cp = 0.01),
rpart.control(minsplit = ms, minbucket= round(ms/2), cp = 0.01))
controls = rbind(c1, c2, c3, c4, c5, c6, c7, c8, c9, c10, c11, c12, c13, c14,
c15, c16, c17, c18, c19, c20, c21, c22, c23, c24, c25, c26, c27,
c28, c29, c30, c200, c250, c300, c350, c400)
table[,2] = c(rep(c("6", "4", "2"), 2), rep(c("8","6", "4", "2"), 11))}
#Get label
{
##Iterations
#change end of loop depending on iteration being tuned
for(r in 1:3)
{
#Different iterative label vector for each iteration
iterlabel <- label.selector(labels,label.tracker)
results[paste("I", r, ".Label", sep = "")] <- iterlabel
train$iterl <- iterlabel[index$train]
test$iterl <- iterlabel[index$test]
valid$iterl <- iterlabel[index$valid]
#Make dataframes work for decision trees
train$data <- data.frame(cbind(train$iterl, train$img))
colnames(train$data)[1] <- "label"
test$data <- data.frame(cbind(test$iterl, test$img))
colnames(test$data)[1] <- "label"
valid$data <- data.frame(cbind(valid$iterl, valid$img))
colnames(valid$data)[1] <- "label"
#THIS IS WHERE CLASSIFICATION ACTUALLY HAPPENS
model <- rpart(formula, method = "class", data = train$data, control = ics[r])
#save this?
results[paste("I", r, ".Pred", sep = "")] <-
as.integer(predict(model, img_fs, type="class"))
#sum labels at used indices
labelsum[[r]] = sum(label.tracker[c(index$train, index$test, index$valid)])
## Update the label tracker
if(r!=4)
{
results[paste("I", r, ".Label.Added", sep = "")] <- FALSE
miss.train <- which(results[index$train,paste("I", r, ".Pred", sep = "")]!=
results[index$train,paste("I", r, ".Label", sep = "")])
#Different calculations of "Actual Label"
miss.rest <- which(results[c(index$test, index$valid) ,paste("I", r, ".Pred", sep = "")]!=
results[c(index$test, index$valid) , "Max.Mode"])
label.tracker[c(miss.train, miss.rest)] <- label.tracker[c(miss.train, miss.rest)]+1
results[c(miss.train, miss.rest), paste("I", r, ".Label.Added", sep = "")] <- TRUE
}
}
#Trials for parameter tuning
r = 4
#Different iterative label vector for each iteration
iterlabel <- label.selector(labels,label.tracker)
results[paste("I", r, ".Label", sep = "")] <- iterlabel
train$iterl <- iterlabel[index$train]
test$iterl <- iterlabel[index$test]
valid$iterl <- iterlabel[index$valid]
#Make dataframes work for decision trees
train$data <- data.frame(cbind(train$iterl, train$img))
colnames(train$data)[1] <- "label"
test$data <- data.frame(cbind(test$iterl, test$img))
colnames(test$data)[1] <- "label"
valid$data <- data.frame(cbind(valid$iterl, valid$img))
colnames(valid$data)[1] <- "label"}
for (i in 1:50){
#THIS IS WHERE CLASSIFICATION ACTUALLY HAPPENS
model <- rpart(formula, method = "class", data = train$data, control = controls[i,])
results[paste("I", r, ".Pred", sep = "")] <- as.integer(predict(model, img_fs, type="class"))
miss.train <- which(results[index$train,paste("I", r, ".Pred", sep = "")]!=
results[index$train,paste("I", r, ".Label", sep = "")])
miss.trM <- which(results[index$train,paste("I", r, ".Pred", sep = "")]!=
results[index$train , "Max.Mode"])
miss.teM <- which(results[index$test ,paste("I", r, ".Pred", sep = "")]!=
results[index$test , "Max.Mode"])
miss.teI <- which(results[index$test ,paste("I", r, ".Pred", sep = "")]!=
results[index$test,paste("I", r, ".Label", sep = "")])
avg <- data.frame(data.frame(matrix(vector(), 50, 5,
dimnames=list(c(), c("tr","trM",
"teI", "teM", "diff")))))
table[i, "tr"] = 1-length(miss.train)/length(index$train)
table[i, "trM"] = 1-length(miss.trM)/length(index$train)
table[i, "teI"] = 1-length(miss.teI)/length(index$test)
table[i, "teM"] = 1-length(miss.teM)/length(index$test)
}
table["diff"] = table["tr"]-table["teI"]
tables[[t]] <- table
}
tables2 = llply(tables, function(df) df[,sapply(df, is.numeric)]) # strip out non-numeric cols
avg = Reduce("+", tables2)/length(tables2)
View(avg)
View(tables)
View(results)