ResourceAllocation/Caret1.R at master · ameliariely/ResourceAllocation · GitHub

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library(caret)

library(mlbench)

#This is super fucked
mymodel <- train.j48$modelInfo
mymodel$parameters <- data.frame(parameter = c("U", "C", "M", "B", "S"), class = c("logical","numeric", "numeric", "logical", "logical"), label = c("U", "C", "M", "B", "S"))

form = as.formula("iterlabel[index] ~ markov1 + markov2 + markov3 + markov4 + markov5 +
                     SDIntensityBG + IntensityDifference + avg.gabor.mean + avg.gabor.SD + Energy + Homogeneity + Entropy +
                     thirdordermoment + Inversevariance + Sumaverage + Variance + Clustertendency + MaxProbability +
                     Circularity + Compactness + Eccentricity + Solidity + Extent + RadialDistanceSD + SecondMoment +
                     Area + ConvexArea + Perimeter + ConvexPerimeter + EquivDiameter + MajorAxisLength +
                     MinorAxisLength")


##Import data
stuff <- read.csv("LIDC dataset with full annotations.csv",header=TRUE)
img_fs <- stuff[,c(5:18, 43:69)]
img_fs <- data.frame(img_fs, Avg.Gabor(stuff))

##Process labels
labels <- stuff[,70:73]
labels <- t(apply(labels,1,sample))
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)

## Label tracker
label.tracker <- rep(1,nrow(labels))

#create partition
index <- createDataPartition(labels[,4], list = FALSE, groups =3, p= .9, times =1)

##Iterations
for(r in 1:4)
{
  set.seed(r)
  iterlabel <- label.selector(labels,label.tracker)
  training <- cbind(iterlabel[index], img_fs[index,])

  tc <- trainControl(method = "cv",verboseIter = TRUE, returnData = TRUE)
  grid <- expand.grid(C=0.15, M=4, B= FALSE, U= FALSE, S = FALSE)


  train.j481 <- train(x = training[,-1],
                     y= training[,1],
                     method=mymodel,
                     tuneGrid = grid,
                     trControl = tc)

  train.j48 <- train(x = training[,-1],
                      y= training[,1],
                      method="J48",
                      #tuneGrid = grid,
                      trControl = tc,)
  train.rpart <- train(x = training[,-1],
                      y= training[,1],
                      method="rpart")
  train.rpart1 <- train(x = training[,-1],
                       y= training[,1],
                       method="rpart",
                       trControl = tc)

  model <- J48(form, training[,-1],
               training[,1])

}