tests/regressionTestScript.R

In SixianZhang/CS499-Coding-Project-1: Nearest neighbors for regression and binary classification

library(NearestNeighbors)

BaselinePredict <- function(y.vec, fold.vec, n.folds) {
  prediction <- rep(NA, n.folds)
  prediction.loss <- rep(NA, n.folds)
  for (fold.i in seq_len(n.folds)) {
    train.index <- which(fold.vec != fold.i)
    validation.index <- which(fold.vec == fold.i)
    prediction[fold.i] <- mean(y.vec[train.index])
    prediction.loss[fold.i] <-
      mean((y.vec[validation.index] - prediction[fold.i]) ^ 2)
  }
  return(prediction.loss)
}

# Data 4: prostate
data(prostate, package = "ElemStatLearn")
X.mat = data.matrix(subset(prostate, select = -c(lpsa, train)))
y.vec = as.vector(prostate$lpsa)
n.folds <- 3L
fold.vec <- sample(rep(1:n.folds, l = nrow(X.mat)))

NN.pred.mat <-
  NNLearnCV(
    X.mat = X.mat,
    y.vec = y.vec,
    fold.vec = fold.vec,
    n.folds = n.folds
  )
NN.loss.vec <-
  NN.pred.mat$train.loss.mat[NN.pred.mat$selected.neighbors, ]

baseline.loss.vec <- BaselinePredict(y.vec, fold.vec, n.folds)
result <- t(cbind(NN.loss.vec, baseline.loss.vec))
rownames(result) <- c("Nearest Neighbors", "Baseline")
colnames(result) <- c("Fold 1", "Fold 2", "Fold 3")
result
max.neighbors <- length(NN.pred.mat$train.loss.vec)
barplot(
  result,
  main = "Regression: prostate",
  xlab = "mean loss value",
  legend = (rownames(result)),
  beside = TRUE
)
matplot(
  y = cbind(NN.pred.mat$validation.loss.vec, NN.pred.mat$train.loss.vec),
  xlab = "neighbors",
  ylab = "mean loss value",
  type = "l",
  lty = 1:2,
  pch = 15,
  col = c(17)
)
dot.x <- NN.pred.mat$selected.neighbors
dot.y <- NN.pred.mat$validation.loss.vec[dot.x]
matpoints(x = dot.x,
          y = dot.y,
          col = 2,
          pch = 19)
legend(
  x = max.neighbors,
  0,
  c("Validation loss", "Train loss"),
  lty = 1:2,
  xjust = 1,
  yjust = 0
)


# #Data 5: ozone
data(ozone, package = "ElemStatLearn")
X.mat = data.matrix(subset(ozone, select = -c(ozone)))
y.vec = as.vector(ozone$ozone)
n.folds <- 3L
fold.vec <- sample(rep(1:n.folds, l = nrow(X.mat)))

NN.pred.mat <-
  NNLearnCV(
    X.mat = X.mat,
    y.vec = y.vec,
    fold.vec = fold.vec,
    n.folds = n.folds
  )
NN.loss.vec <-
  NN.pred.mat$train.loss.mat[NN.pred.mat$selected.neighbors, ]

baseline.loss.vec <- BaselinePredict(y.vec, fold.vec, n.folds)
result <- t(cbind(NN.loss.vec, baseline.loss.vec))
rownames(result) <- c("Nearest Neighbors", "Baseline")
colnames(result) <- c("Fold 1", "Fold 2", "Fold 3")
result
max.neighbors <- length(NN.pred.mat$train.loss.vec)
barplot(
  result,
  main = "Regression: ozone",
  xlab = "mean loss value",
  legend = (rownames(result)),
  beside = TRUE
)
matplot(
  y = cbind(NN.pred.mat$validation.loss.vec, NN.pred.mat$train.loss.vec),
  xlab = "neighbors",
  ylab = "mean loss value",
  type = "l",
  lty = 1:2,
  pch = 15,
  col = c(17)
)
dot.x <- NN.pred.mat$selected.neighbors
dot.y <- NN.pred.mat$validation.loss.vec[dot.x]
matpoints(x = dot.x,
          y = dot.y,
          col = 2,
          pch = 19)
legend(
  x = max.neighbors,
  0,
  c("Validation loss", "Train loss"),
  lty = 1:2,
  xjust = 1,
  yjust = 0
)