Scripts/tt.R

In SixianZhang/CS499-Coding-Project-3: Neural network for regression and binary classification

# library(ElemStatLearn)
# library(NeuralNetwork)
# 
# data(spam, package = "ElemStatLearn")
# data(prostate, package = "ElemStatLearn")
# data(ozone, package = "ElemStatLearn")
# X.mat <- as.matrix(spam[,1:57])
# y.vec <- as.vector(spam$spam)
# y.vec <- ifelse(y.vec == "spam", 1, -1)
# 
# # X.mat <- as.matrix(prostate[,1:8])
# # y.vec <- as.vector(prostate$lpsa)
# 
# # X.mat <- as.matrix(ozone[, -1])
# # y.vec <- as.vector(ozone[, 1])
# 
# if (all(y.vec %in% c(-1,1)))
# {
#   is.binary <- 1
# }else
# {
#   is.binary <- 0  
# }
# 
# result.list <- NNetEarlyStoppingCV(X.mat, y.vec, max.iterations = 500L, step.size = 0.06, 
#                                    n.hidden.units = 200L, n.folds = 6L)
# 
# matplot(1:500,as.matrix(result.list$mean.validation.loss.vec), type = 'l', lty = 1:2,
#         pch = 15,
#         col = c(17))

library(ElemStatLearn)
library(NeuralNetwork)

data(spam, package = "ElemStatLearn")
data(SAheart, package = "ElemStatLearn")
data(zip.train, package = "ElemStatLearn")
zip.train <- zip.train[zip.train[, 1] %in% c(0, 1),]
data(prostate, package = "ElemStatLearn")
data(ozone, package = "ElemStatLearn")

data.list <- list(
  spam = list(
    features = as.matrix(spam[, 1:57]),
    labels = ifelse(spam$spam == "spam", 1, -1),
    step.size = 0.06,
    is.01 = TRUE
  ),
  
  SAheart = list(
    features = as.matrix(SAheart[, c(1:4, 6:9)]),
    labels = ifelse(SAheart$chd == 1, 1, -1),
    step.size = 0.02,
    
    is.01 = TRUE
  ),
  
  zip.train = list(
    features = as.matrix(zip.train[,-1]),
    labels = ifelse(zip.train[, 1] == 1, 1, -1),
    step.size = 0.05,
    
    is.01 = TRUE
  ),
  
  
  prostate = list(
    features = as.matrix(prostate[, 1:8]),
    labels = prostate$lpsa,
    step.size = 0.02,
    
    is.01 = FALSE
  ),
  
  ozone = list(
    features = as.matrix(ozone[,-1]),
    labels = ozone[, 1],
    step.size = 0.02,
    
    is.01 = FALSE
  ))

n.folds <- 5L
n.hidden.units <- 100L
max.iterations <- 50L

data.name = 3
data.set <- data.list[[data.name]]

test.loss.mat <- matrix(0, nrow = n.folds, ncol = 2)
step.size <- data.set$step.size

#Check data type here:

set.seed(1)

fold.vec <- sample(rep(1:n.folds, l = length(data.set$labels)))

for (i.fold in (1:n.folds)) {
  train.index <- fold.vec != i.fold
  test.index <- fold.vec == i.fold
  
  X.train <- data.set$features[train.index,]
  y.train <- data.set$labels[train.index]
  X.test <- data.set$features[test.index,]
  y.test <- data.set$labels[test.index]
  
  result.list <- NNetEarlyStoppingCV(
    X.mat = X.train,
    y.vec = y.train,
    max.iterations = max.iterations,
    step.size = step.size,
    n.hidden.units = n.hidden.units
  )
  
  if (data.set$is.01) {
    # binary data
    NNet.predict <-
      ifelse(result.list$predict(X.test) > 0.5, 1,-1)
    NNet.loss <- mean(ifelse(NNet.predict == y.test, 0, 1))
    
    baseline.predict <- mean(y.test)
    
    
  } else{
    # regression data
    NNet.predict <- result.list$predict(X.test)
    NNet.loss <- mean((NNet.predict - y.test) ^ 2)
    
    baseline.predict <- mean(y.test)
    
  }
  
  
  baseline.loss <- mean((baseline.predict - y.test) ^ 2)
  
  test.loss.mat[i.fold,] = c(NNet.loss, baseline.loss)
}

colnames(test.loss.mat) <- c("Nerual Network", "Baseline")

### Matrix of loss values
# plot result
if (!data.set$is.01) {
  barplot(
    test.loss.mat,
    main = c("Square Regression: ", "zip.train"),
    xlab = "mean loss value",
    legend = (rownames(test.loss.mat)),
    beside = TRUE
  )
} else{
  barplot(
    test.loss.mat,
    main = c("Binary Classification: ", "zip.train"),
    xlab = "mean loss value",
    legend = (rownames(test.loss.mat)),
    beside = TRUE
  )
}

model.list <- NNetEarlyStoppingCV(
  X.mat = X.train,
  y.vec = y.train,
  max.iterations = max.iterations,
  step.size = step.size,
  n.hidden.units = n.hidden.units
)