scripts/wine-best-mowRandomForest.R

In vonHousen/mowRandomForest: Custom implementation of Random Forest based on rpart library.

# Load data
wineWhite = read.csv("data/WINE/winequality-white.csv", header = TRUE, sep = ";")
wineRed = read.csv("data/WINE/winequality-red.csv", header = TRUE, sep = ";")


dataset <- rbind(wineWhite,wineRed)
dataset$quality <- as.factor(dataset$quality)


dataset <- dataset[sample(nrow(dataset)),]
train_indices <- 1:round(0.7 * nrow(dataset))
trainset <- dataset[train_indices,]
test_indices <- round(0.7 * nrow(dataset)):nrow(dataset)
testset <- dataset[test_indices,]


library(caret)
library(devtools)
install('.')
library(mowRandomForest)
library(rpart)
library(parallel)


#complexity test
print(" ======== COMPLEXITY TEST ========")
print("ldrzew	subset	z")
print("20	0.6	0.3")
print("complexity	0.1	0.05	0.01	0.005	0.001	0.0005 -1")
complexity <- c(0.05,	0.01,	0.005,	0.001,	0.0005, -1)
for (comp_i in complexity) {
  print(system.time(
    {
  print("complexity")
  print(comp_i)
  mowForest <- mowRandomForest(
    df = trainset,
    formula = salary ~.,
    ntree = 20,
    complexity = 0.01,
    subsetRatio = 0.6,
    zratio = 0.3
  )
  mow_forest_preeds <- predict(mowForest,testset)
  print(confusionMatrix(factor(testset$salary),factor(mow_forest_preeds)))
    }
  ))
}

#subset test
print(" ======== SUBSET RATIO TEST ========")
print("ldrzew	z	comlexity")
print("20	0.3	0.01")
print("subset	0.1	0.2	0.4	0.6	0.8")
subset <- c(0.1,	0.2,	0.4,	0.6,	0.8)
for (sub_i in subset) {
  print(system.time(
    {
      print("complexity")
      print(comp_i)
      mowForest <- mowRandomForest(
        df = trainset,
        formula = salary ~.,
        ntree = 20,
        complexity = 0.01,
        subsetRatio = sub_i,
        zratio = 0.3
      )
      mow_forest_preeds <- predict(mowForest,testset)
      print(confusionMatrix(factor(testset$salary),factor(mow_forest_preeds)))
    }
  ))
}

#l drzew test
print(" ======== l DRZEW TEST ========")
print("z	subset	comlexity")
print("0.3	0.6	0.01")
print("l drzew 10	20	40	50	100	200")
ntree <- c(10,	20,	40,	50,	100,	200)
for (n_i in ntree) {
  print(system.time(
    {
      print("complexity")
      print(comp_i)
      mowForest <- mowRandomForest(
        df = trainset,
        formula = salary ~.,
        ntree = ntree,
        complexity = 0.01,
        subsetRatio = 0.6,
        zratio = 0.3
      )
      mow_forest_preeds <- predict(mowForest,testset)
      print(confusionMatrix(factor(testset$salary),factor(mow_forest_preeds)))
    }
  ))
}


#z test
print(" ======== Z RATIO TEST ========")
print("ldrzew	subset	comlexity")
print("20	0.6	0.01")
print("subset	0.6, 0.3, 0.1")
z <- c(0.6, 0.3, 0.1)
for (z_i in z) {
  print(system.time(
    {
      print("complexity")
      print(comp_i)
      mowForest <- mowRandomForest(
        df = trainset,
        formula = salary ~.,
        ntree = 20,
        complexity = 0.01,
        subsetRatio = 0.6,
        zratio = z
      )
      mow_forest_preeds <- predict(mowForest,testset)
      print(confusionMatrix(factor(testset$salary),factor(mow_forest_preeds)))
    }
  ))
}