Multilabel classification using R and the neuralnet package

mtlbl_clf.R

################################################################################
# Loading data

rm( list=ls() )

# load libs
require(neuralnet)
require(nnet)

# Load data and set names
wines <- read.csv("wines.csv")
names(wines) <- c("label",
                  "Alcohol",
                  "Malic_acid",
                  "Ash",
                  "Alcalinity_of_ash",
                  "Magnesium",
                  "Total_phenols",
                  "Flavanoids",
                  "Nonflavanoid_phenols",
                  "Proanthocyanins",
                  "Color_intensity",
                  "Hue",
                  "OD280_OD315_of_diluted_wines",
                  "Proline")

################################################################################
# Preprocessing

# Encode as a one hot vector multilabel data
train <- cbind(wines[, 2:14], class.ind(as.factor(wines$label)))
# Set labels name
names(train) <- c(names(wines)[2:14],"l1","l2","l3")

# Scale data
scl <- function(x){ (x - min(x))/(max(x) - min(x)) }
train[, 1:13] <- data.frame(lapply(train[, 1:13], scl))
head(train)

################################################################################
# Fitting

# Set up formula
n <- names(train)
f <- as.formula(paste("l1 + l2 + l3 ~", paste(n[!n %in% c("l1","l2","l3")], collapse = " + ")))
f

nn <- neuralnet(f,
                data = train,
                hidden = c(13,10,3),
                act.fct = "logistic",
                linear.output = FALSE,
                lifesign = "minimal")

summary(nn)

# Compute predictions
pr.nn <- compute(nn, train[, 1:13])

# Extract results
pr.nn_ <- pr.nn$net.result
head(pr.nn_)

# Accuracy (training set)
original_values <- max.col(train[, 14:16])
pr.nn_2 <- max.col(pr.nn_)
mean(pr.nn_2 == original_values)

################################################################################
# Crossvalidate
set.seed(10)
k <- 10
outs <- NULL
proportion <- 0.995

library(plyr) 
pbar <- create_progress_bar('text')
pbar$init(k)

for(i in 1:k)
{
    index <- sample(1:nrow(train), round(proportion*nrow(train)))
    train_cv <- train[index, ]
    test_cv <- train[-index, ]
    nn_cv <- neuralnet(f,
                        data = train_cv,
                        hidden = c(13, 10, 3),
                        act.fct = "logistic",
                        linear.output = FALSE)
    
    # Compute predictions
    pr.nn <- compute(nn_cv, test_cv[, 1:13])
    
    # Extract results
    pr.nn_ <- pr.nn$net.result
    
    # Accuracy (test set)
    original_values <- max.col(test_cv[, 14:16])
    pr.nn_2 <- max.col(pr.nn_)
    outs[i] <- mean(pr.nn_2 == original_values)
    pbar$step()
}

# Average accuracy
mean(outs)

Do you think it needs be rounded in Accuracy part?